Relevant bibliographies by topics / Pima Indian Diabetes (PID)

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Pima Indian Diabetes (PID)'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Pima Indian Diabetes (PID)"

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Dwianti, Westari, and Abdul Halim Dr. "Performa Comparison of the K-Means Method for Classification in Diabetes Patients Using Two Normalization Methods." International Journal of Multidisciplinary Research and Analysis 04, no. 01 (2021): 18–23. https://doi.org/10.47191/ijmra/v4-i1-03.

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The diabetes classification system is very useful in the health sector. This paper discusses the classification system for diabetes using the K-Means algorithm. The Pima Indian Diabetes (PID) dataset is used to train and evaluate this algorithm. The unbalanced value range in the attributes affects the quality of the classification result, so it is necessary to preprocess the data which is expected to improve the accuracy of the PID dataset classification result. Two types of preprocessing methods are used that are min-max normalization and z-score normalization. These two normalization methods are used and the classification accuracies are compared. Before the data classification process is carried out, the data is divided into training data and test data. The result of the classification test using the K-Means algorithm has shown that the best accuracy lies in the PID dataset which has been normalized using the min-max normalization method, which 79% compared to z-score normalization.    
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Zamil, Mohammed F., Dunia H. Hameed, and Usama Samir Mahmoud. "A Comprehensive Data Enhancement Method for the Pima Dataset to Improve Diabetes Prediction Performance." Journal Port Science Research 8, no. 4 (2025): 314–20. https://doi.org/10.36371/port.2025.4.1.

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Diabetes is one of the silent killer diseases that can effect if left without medication and a real change in lifestyle. 10.5% of adult people (10-79 years) have diabetic in the world according to the International Diabetes Federation (IDF) Diabetes Atlas (2021) reports [1]. And number getting higher. Thus, in this study, we aim to build a prediction model using Pima Indian Diabetes (PID) dataset. Dataset required heavy-duty processing because of its low-quality characteristics, such as lot missing values and imbalance. This paper shows how enhancing data quality can affectively reflect on models’ performance. Based on the conducted experiments, ensemble models such as Random Forest show highest performance (0.86% AUC-ROC) with highest encasement among all other model by around 4%.
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Aman and Singh Chhillar Rajender. "Optimized stacking ensemble for early-stage diabetes mellitus prediction." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 7048–55. https://doi.org/10.11591/ijece.v13i6.pp7048-7055.

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This paper presents an optimized stacking-based hybrid machine learning approach for predicting early-stage diabetes mellitus (DM) using the PIMA Indian diabetes (PID) dataset and early-stage diabetes risk prediction (ESDRP) dataset. The methodology involves handling missing values through mean imputation, balancing the dataset using the synthetic minority over-sampling technique (SMOTE), normalizing features, and employing a stratified train-test split. Logistic regression (LR), naïve Bayes (NB), AdaBoost with support vector machines (AdaBoost+SVM), artificial neural network (ANN), and k-nearest neighbors (k-NN) are used as base learners (level 0), while random forest (RF) meta-classifier serves as the level 1 model to combine their predictions. The proposed model achieves impressive accuracy rates of 99.7222% for the ESDRP dataset and 94.2085% for the PID dataset, surpassing existing literature by absolute differences ranging from 10.2085% to 16.7222%. The stacking-based hybrid model offers advantages for early-stage DM prediction by leveraging multiple base learners and a meta-classifier. SMOTE addresses class imbalance, while feature normalization ensures fair treatment of features during training. The findings suggest that the proposed approach holds promise for early-stage DM prediction, enabling timely interventions and preventive measures.
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Zhao, Jian, Hanlin Gao, Chen Yang, Tianbo An, Zhejun Kuang, and Lijuan Shi. "Attention-Oriented CNN Method for Type 2 Diabetes Prediction." Applied Sciences 14, no. 10 (2024): 3989. http://dx.doi.org/10.3390/app14103989.

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Diabetes is caused by insulin deficiency or impaired biological action, and long-term hyperglycemia leads to a variety of tissue damage and dysfunction. Therefore, the early prediction of diabetes and timely intervention and treatment are crucial. This paper proposes a robust framework for the prediction and diagnosis of type 2 diabetes (T2DM) to aid in diabetes applications in clinical diagnosis. The data-preprocessing stage includes steps such as outlier removal, missing value filling, data standardization, and assigning class weights to ensure the quality and consistency of the data, thereby improving the performance and stability of the model. This experiment used the National Health and Nutrition Examination Survey (NHANES) dataset and the publicly available PIMA Indian dataset (PID). For T2DM classification, we designed a convolutional neural network (CNN) and proposed a novel attention-oriented convolutional neural network (SECNN) through the channel attention mechanism. To optimize the hyperparameters of the model, we used grid search and K-fold cross-validation methods. In addition, we also comparatively analyzed various machine learning (ML) models such as support vector machine (SVM), logistic regression (LR), decision tree (DT), random forest (RF), and artificial neural network (ANN). Finally, we evaluated the performance of the model using performance evaluation metrics such as precision, recall, F1-Score, accuracy, and AUC. Experimental results show that the SECNN model has an accuracy of 94.12% on the NHANES dataset and an accuracy of 89.47% on the PIMA Indian dataset. SECNN models and CNN models show significant improvements in diabetes prediction performance compared to traditional ML models. The comparative analysis of the SECNN model and the CNN model has significantly improved performance, further verifying the advantages of introducing the channel attention mechanism. The robust diabetes prediction framework proposed in this article establishes an effective foundation for diabetes diagnosis and prediction, and has a positive impact on the development of health management and medical industries.
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Kangra, Kirti, and Jaswinder Singh. "Comparative analysis of predictive machine learning algorithms for diabetes mellitus." Bulletin of Electrical Engineering and Informatics 12, no. 3 (2023): 1728–37. http://dx.doi.org/10.11591/eei.v12i3.4412.

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Diabetes mellitus (DM) is a serious worldwide health issue, and its prevalence is rapidly growing. It is a spectrum of metabolic illnesses defined by perpetually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of problems, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be particularly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to identify the strong ML algorithm to predict it. For this several ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to studied work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the experiment was performed using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This article discussed about performance matrices and error rates of classifiers for both datasets. The results showed that for PID database (PIDD), SVM works better with an accuracy of 74% whereas for Germany KNN and RF work better with 98.7% accuracy. This study can aid healthcare facilities and researchers in comprehending the value and application of ML algorithms in predicting diabetes at an early stage.
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Kirti, Kangra, and Singh Jaswinder. "Comparative analysis of predictive machine learning algorithms for diabetes mellitus." Bulletin of Electrical Engineering and Informatics 12, no. 3 (2023): 1728~1737. https://doi.org/10.11591/eei.v12i3.4412.

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Diabetes mellitus (DM) is a serious worldwide health issue, and its prevalence is rapidly growing. It is a spectrum of metabolic illnesses defined by perpetually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of problems, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be particularly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to identify the strong ML algorithm to predict it. For this several ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to studied work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the experiment was performed using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This article discussed about performance matrices and error rates of classifiers for both datasets. The results showed that for PID database (PIDD), SVM works better with an accuracy of 74% whereas for Germany KNN and RF work better with 98.7% accuracy. This study can aid healthcare facilities and researchers in comprehending the value and application of ML algorithms in predicting diabetes at an early stage.
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Aman, Aman, and Rajender Singh Chhillar. "Optimized stacking ensemble for early-stage diabetes mellitus prediction." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (2023): 7048. http://dx.doi.org/10.11591/ijece.v13i6.pp7048-7055.

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<div align="center"><span lang="EN-US">This paper presents an optimized stacking-based hybrid machine learning approach for predicting early-stage diabetes mellitus (DM) using the PIMA Indian diabetes (PID) dataset and early-stage diabetes risk prediction (ESDRP) dataset. The methodology involves handling missing values through mean imputation, balancing the dataset using the synthetic minority over-sampling technique (SMOTE), normalizing features, and employing a stratified train-test split. Logistic regression (LR), naïve Bayes (NB), AdaBoost with support vector machines (AdaBoost+SVM), artificial neural network (ANN), and k-nearest neighbors (k-NN) are used as base learners (level 0), while random forest (RF) meta-classifier serves as the level 1 model to combine their predictions. The proposed model achieves impressive accuracy rates of 99.7222% for the ESDRP dataset and 94.2085% for the PID dataset, surpassing existing literature by absolute differences ranging from 10.2085% to 16.7222%. The stacking-based hybrid model offers advantages for early-stage DM prediction by leveraging multiple base learners and a meta-classifier. SMOTE addresses class imbalance, while feature normalization ensures fair treatment of features during training. The findings suggest that the proposed approach holds promise for early-stage DM prediction, enabling timely interventions and preventive measures.</span></div>
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Al-Nussairi, Maryam Mohammed, and Mohammad Ali H. Eljinini. "A Hybrid Approach for Enhancing the Classification Accuracy for Diabetes Disease." Journal of Information Technology Research 15, no. 1 (2022): 1–18. http://dx.doi.org/10.4018/jitr.298024.

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This paper proposes a new training algorithm for artificial neural networks based on an enhanced version of the grey wolf optimizer (GWO) algorithm. The proposed model is used for classifying the patients of diabetes disease. The results showed that the proposed training algorithm enhanced the performance of ANNs with a better classification accuracy as compared to the other state of art training algorithms for the classification of diabetes on publicly available “Pima Indian Diabetes (PID) dataset”. Several experiments have been executed on this dataset with variation in size of the population, techniques to handle missing data, and their impact on classification accuracy has been discussed. Finally, the results are compared with other nature-inspired algorithms trained ANN. EGWO attained better results in terms of classification accuracy than the other algorithms. The convergence curve proved that EGWO had balanced the local and global search abilities because it was faster to reach better positions than the original GWO.
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CHALO, Sarbast, and İbrahim Berkan AYDİLEK. "A New Preprocessing Method for Diabetes and Biomedical Data Classification." Qubahan Academic Journal 2, no. 4 (2023): 6–18. http://dx.doi.org/10.48161/qaj.v2n4a135.

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People of all ages and socioeconomic levels, all over the world, are being diagnosed with type 2 diabetes at rates that are higher than they have ever been. It is possible for it to be the root cause of a wide variety of diseases, the most notable of which include blindness, renal illness, kidney disease, and heart disease. Therefore, it is of the utmost importance that a system is devised that, based on medical information, is capable of reliably detecting patients who have diabetes. We present a method for the identification of diabetes that involves the training of the features of a deep neural network between five and 10 times using the cross-validation training mode. The Pima Indian Diabetes (PID) data set was retrieved from the database that is part of the machine learning repository at UCI. In addition, the results of ten-fold cross-validation show an accuracy of 97.8%, a recall OF 97.8%, and a precision of 97.8% for PIMA dataset using RF algorithm. This research examined a variety of other biomedical datasets to demonstrate that machine learning may be used to develop an efficient system that can accurately predict diabetes. Several different types of machine learning classifiers, such as KNN, J48, RF, and DT, were utilized in the experimental findings of biological datasets. The findings that were obtained demonstrated that our trainable model is capable of correctly classifying biomedical data. This was demonstrated by achieving higher 99% accuracy, recall, and precision for parikson dataset.
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Kolo, Silue, Johnson Grace Y. Edwige, Konan K. Hyacinthe, Asseu Olivier, and Bourget Daniel. "PREDICTIVE ANALYSIS OF DIABETES WITHOUT DATA PRE-PROCESSING VIA THE EVALUATION OF TREE ALGORITHMS." International Journal of Advanced Research 10, no. 12 (2022): 1059–69. http://dx.doi.org/10.21474/ijar01/15940.

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Diabetes is a common disease, incurable and fatal in its complication phases. Its management, like many other metabolic diseases, remains a scientific challenge. Mathematical approaches have been used to understand this scourge and artificial intelligence is used to model its prediction. In general, the effectiveness and efficiency of an artificial intelligence solution depends on the nature and characteristics of the data and the performance of the learning methods. Hence the interest in the quality of the data and the performance of the methods used to model such a task. In order to find a suitable artificial intelligence model for diabetes prediction, several studies have used methods from different techniques. Thus, diabetes prediction has been addressed using machine learning methods, neural networks, deep learning, Bayesian naive classification, K-nearest neighbors and machine vector support. In order to compare the performance to determine the best model, several of these methods are analyzed in previous studies. Thus, this paper evaluates the methods based on the decision tree technique (DT, RF, LightGBM, Adaboost and XGBoost), based on the PIMA Diabetes Indian data (PID). The aim is to show the predictive ability of the methods of this technique and to determine the appropriate method for predicting diabetes with raw data. The PIMA data are described statistically, and the comparative analysis of the models is performed following K-fold cross-validation, before and after class balancing. At the end of the experiment, the best results are obtained by LightGBM, XGBoostand RF on different metrics.
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Dissertations / Theses on the topic "Pima Indian Diabetes (PID)"

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Quintana, Chimal Benjamín de Jesús. "Comparación entre regresión logística y perceptrón multicapa: Caso aplicado al conjunto de datos Pima Indian Database." Tesis de Licenciatura, Universidad Autónoma del Estado de México, 2021. http://hdl.handle.net/20.500.11799/111526.

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El objetivo del trabajo fue realizar una comparación de las propiedades clasificatorias entre una regresión logística y un perceptrón multicapa aplicado a un problema dicotómico como es el diagnóstico positivo o negativo de diabetes. Los datos utilizados son del Pima Indian Diabetes Database que son propiedad del National Institute of Diabetes and Digestive and Kidney Diseases de los Estados Unidos de América. La metodología fue en el desarrollo de los algoritmos de la regresión logística y la construcción de un perceptrón multicapa en el lenguaje Python. LOs resultados muestran la superioridad clasificatoria del perceptrón multicapa por lo cual se sugiere su uso en problemas de clasificación.
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Natarajan, Keerthana. "Integrating Machine Learning with Web Application to Predict Diabetes." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627663657558303.

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Moffett, Carol D. "The Impact of Childhood Measures of Glycemia and Insulin Resistance Factors on Follow-Up Glycemic Measures." Diss., The University of Arizona, 2007. http://hdl.handle.net/10150/194096.

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The purpose of this research was to evaluate the impact of glycemic measures, and changes in identified risk factors (BMI, waist circumference, lipids, blood pressure) on follow-up glycemia, in Pima children at high risk for type two diabetes (type 2 DM).I computed incidence and cumulative incidence of type 2 DM in Pima children 5-19 years of age between 1983 and 2004. Cox proportional hazards rates for development of type 2 DM were calculated by glycemic measure (HbA1C, 20PG, FPG) controlling for confounding factors (age, sex, BMI, blood pressure, and cholesterol). Diabetes was defined by the presence of at least one of four criteria: 1) 20PG of >200 mg/dl, 2) FPG of >126 mg/dl, 3) HbA1C > 8.0%, or 4) hypoglycemic treatment. Linear regression models were computed to identify the impact of changes in risk factors on changes in HbA1C. Only exams performed in non-diabetic children during childhood were included in the regression models.Among 2658 non-diabetic children, 258 cases of diabetes occurred during mean 9.1 years of follow-up (1.5 - 21.7). The age-sex adjusted incident rate of diabetes was 19.0 cases per 1000 person-years, and cumulative incidence was 54% by age 40. Incidence rates increased with increasing baseline values of 20PG, and FPG, but not for HbA1C. For HbA1C the relationship was u-shaped with the lowest and highest quartiles having the highest DM rates. After adjustment for confounding risk factors using Cox proportional hazards analysis, the risk for diabetes increased 2-fold for every 10 mg/dl increase in FPG. Changes in waist circumference best predicted changes in HbA1C (R2 = 0.48, Ï <0.001). However, the ability of waist circumference to predict change is limited due to the powerful effect of regression to the mean, suggesting that these risk factors contribute very little to changes in HbA1C, at least in childhood.Childhood levels of glycemia predict development of type 2 DM later in life. While changes in waist circumference are associated with only moderate changes in HbA1C, this does not refute the significant contribution of adiposity in childhood to the development of type 2 DM.
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Book chapters on the topic "Pima Indian Diabetes (PID)"

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Pujari, P. "Classification of Pima Indian Diabetes Dataset Using Support Vector Machine with Polynomial Kernel." In Deep Learning, Machine Learning and IoT in Biomedical and Health Informatics. CRC Press, 2022. http://dx.doi.org/10.1201/9780367548445-5.

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Arora, Aditi, Chirag Sehgal, Shilpi Sharma, Soumya Ranjan Nayak, and Raghvendra Kumar. "Application of Data Mining Methods and Techniques for Diabetes Prediction Using Pima Indian Dataset." In Proceedings of 3rd International Conference on Smart Computing and Cyber Security. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0573-3_47.

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Nivetha, S., B. Valarmathi, K. Santhi, and T. Chellatamilan. "Detection of Type 2 Diabetes Using Clustering Methods – Balanced and Imbalanced Pima Indian Extended Dataset." In Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2019). Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-43192-1_69.

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Sowmyayani, S. "Predictive Analysis of Diabetes Prediction." In Advances in Computational Intelligence and Robotics. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-4252-7.ch004.

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Deep learning algorithms are used in several applications. The development of technology should help humans in saving their life. Diabetes prediction is crucial for medical practitioners. In this chapter, diabetes is predicted using ensemble learning from the medical data. Three classifiers, Naive Bayes (NB), Random Forest (RF) and Neural Network (NN) are selected and defined three estimators for stacking. Grid search is performed to find the best hyperparameters of each estimator. The Stacking Classifier is then used to combine the predictions of the three base estimators and make the final prediction using Logistic Regression as the meta-classifier. This work also makes use of Synthetic Minority Over-sampling Technique (SMOTE) method for data balancing. The Linear Support Vector Classifier (LSVC) is used to score the features. The proposed method is tested on Pima Indian Diabetes (PID) dataset with several experiments. The proposed method achieved highest accuracy of 98.2% with 0.99AUC. The impact of data balancing, feature selection and ensemble learning are also studied.
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Dharwadkar, Nagaraj V., Shivananda R. Poojara, and Anil K. Kannur. "Risk Analysis of Diabetic Patient Using Map-Reduce and Machine Learning Algorithm." In Advances in Data Mining and Database Management. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3053-5.ch014.

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Diabetes is one of the four non-communicable diseases causing maximum deaths all over the world. The numbers of diabetes patients are increasing day by day. Machine learning techniques can help in early diagnosis of diabetes to overcome the influence of it. In this chapter, the authors proposed the system that imputes missing values present in diabetes dataset and parallel process diabetes data for the pattern discovery using Hadoop-MapReduce-based C4.5 machine learning algorithm. The system uses these patterns to classify the patient into diabetes and non-diabetes class and to predict risk levels associated with the patient. The two datasets, namely Pima Indian Diabetes Dataset (PIDD) and Local Diabetes Dataset (LDD), are used for the experimentation. The experimental results show that C4.5 classifier gives accuracy of 73.91% and 79.33% when applied on (PIDD) (LDD) respectively. The proposed system will provide an effective solution for early diagnosis of diabetes patients and their associated risk level so that the patients can take precaution and treatment at early stages of the disease.
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Leema N., Khanna H. Nehemiah, Elgin Christo V. R., and Kannan A. "Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms." In Research Anthology on Artificial Neural Network Applications. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-2408-7.ch009.

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Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.
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Choubey, Dilip Kumar, Sanchita Paul, Kanchan Bala, Manish Kumar, and Uday Pratap Singh. "Implementation of a Hybrid Classification Method for Diabetes." In Intelligent Innovations in Multimedia Data Engineering and Management. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7107-0.ch009.

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This chapter presents a best classification of diabetes. The proposed approach work consists in two stages. In the first stage the Pima Indian diabetes dataset is obtained from the UCI repository of machine learning databases. In the second stage, the authors have performed the classification technique by using fuzzy decision tree on Pima Indian diabetes dataset. Then they applied PSO_SVM as a feature selection technique followed by the classification technique by using fuzzy decision tree on Pima Indian diabetes dataset. In this chapter, the optimization of SVM using PSO reduces the number of attributes, and hence, applying fuzzy decision tree improves the accuracy of detecting diabetes. The hybrid combinatorial method of feature selection and classification needs to be done so that the system applied is used for the classification of diabetes.
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Mallikarjuna, Basetty, Supriya Addanke, and Anusha D. J. "An Improved Deep Learning Algorithm for Diabetes Prediction." In Advances in Wireless Technologies and Telecommunication. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-7685-4.ch007.

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This chapter introduces the novel approach in deep learning for diabetes prediction. The related work described the various ML algorithms in the field of diabetic prediction that has been used for early detection and post examination of the diabetic prediction. It proposed the Jaya-Tree algorithm, which is updated as per the existing random forest algorithm, and it is used to classify the two parameters named as the ‘Jaya' and ‘Apajaya'. The results described that Pima Indian diabetes dataset 2020 (PIS) predicts diabetes and obtained 97% accuracy.
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Soumya K N and Vigneshwaran P. "Prediction on Type-2 Diabetes Mellitus Using Machine Learning Methods." In Advances in Parallel Computing Algorithms, Tools and Paradigms. IOS Press, 2022. http://dx.doi.org/10.3233/apc220005.

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Diabetes has become one of the most fatal diseases as a result of lifestyle changes, food habits, and decreased physical exercise. Diabetes is believed to afflict 422 million people globally, according to the latest WHO estimates. Having said that, the Type II category of diabetes is more fatal because it is determined by the body’s insulin resistance. Furthermore, Type II diabetes has been linked to complications with the kidneys, eyes, and heart. A big number of scientists are also looking into the possibility of a link between diabetes and cancer. We present an overview of such discoveries as well as our cancer research efforts in this report. Dimensionality reduction, Classification, and Clustering are applied in the proposed work to compare with the existing classifiers. PIMA Indian diabetes datasets and Stanford AIM-94 dataset is considered as the benchmark dataset for performing experimentation.
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Padierna Luis C., González Martha B., and Romero Leoncio A. "PAC-Means: clustering algorithm based on c-Means technique and associative memories." In Ambient Intelligence and Smart Environments. IOS Press, 2012. https://doi.org/10.3233/978-1-61499-080-2-37.

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In this study a partitional clustering technique is proposed. Our proposal is a variant of the c-Means algorithm that replaces its traditional minimum-distance classifier by a classifier based on associative memories. The variant was compared against the original version by applying both techniques to three datasets belonging to the UCI Machine Learning Repository: Iris, Wine and Pima Indian Diabetes. As a comparison criterion, an intracluster-spread index was used. Results obtained in experimental tests show that, when applied to certain databases, the PAC-Means technique overcomes to the c-Means algorithm.
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Conference papers on the topic "Pima Indian Diabetes (PID)"

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M, Manjula B., Harshavardhan A, Indrasena B.T, and Neha Annie S. "Exploratory Data Analysis and Predictive Modeling of Pima Indian Diabetes." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS). IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721874.

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G, Arun, Pundru Chandra Shaker Reddy, Nagaveni Budati, B. Rebecca, Subhadra Perumalla, and Vijay Bhanudas Gujar. "A Deep Learning Framework for Diabetes Prediction: PIMA Indian Dataset." In 2024 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE, 2024. https://doi.org/10.1109/icses63760.2024.10910386.

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Mishra, Soumya Ranjan, and Sachikanta Dash. "Machine Learning Based Diabetes Prediction Using the PIMA Indian Dataset." In 2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES). IEEE, 2024. https://doi.org/10.1109/scopes64467.2024.10991027.

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N, Anushree, Shamyuktha V P, Tanuja K T, and J. Dhivya. "Predictive Modeling for Diabetes Diagnosis Using Diagnostic Measures: A Study on Female Pima Indian Patients." In 2025 3rd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2025. https://doi.org/10.1109/icaeca63854.2025.11012339.

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Patil, Vijaykumar, and D. R. Ingle. "Expression of Concern for: Comparative Analysis of Different ML Classification Algorithms with Diabetes Prediction through Pima Indian Diabetics Dataset." In 2021 International Conference on Intelligent Technologies (CONIT). IEEE, 2021. http://dx.doi.org/10.1109/conit51480.2021.10703017.

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Kumar, Sambhu, Prashant Kumar Singh, Pankaj Kushwaha, and Sanjeev Kumar Prasad. "Early Diabetes Prediction Using CNN-LSTM and CNN-Bi-LSTM Models Optimized with Adam on the PIMA Indian Dataset." In 2024 2nd International Conference on Advancements and Key Challenges in Green Energy and Computing (AKGEC). IEEE, 2024. https://doi.org/10.1109/akgec62572.2024.10868093.

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Guan, Yining, Chia Jung Tsai, and Shuyuan Zhang. "Research on Diabetes Prediction Model of Pima Indian Females." In ISAIMS 2023: 2023 4th International Symposium on Artificial Intelligence for Medicine Science. ACM, 2023. http://dx.doi.org/10.1145/3644116.3644168.

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Shabtari, Morteza Mohammad, Vinod Kumar Shukla, Harendra Singh, and Ipseeta Nanda. "Analyzing PIMA Indian Diabetes Dataset through Data Mining Tool ‘RapidMiner’." In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE, 2021. http://dx.doi.org/10.1109/icacite51222.2021.9404741.

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"COMPARISON OF DIFFERENT CLASSIFICATION TECHNIQUES ON PIMA INDIAN DIABETES DATA." In 13th International Conference on Enterprise Information Systems. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003496803650368.

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Kala, Rahul, Anupam Shukla, and Ritu Tiwari. "Comparative analysis of intelligent hybrid systems for detection of PIMA indian diabetes." In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC). IEEE, 2009. http://dx.doi.org/10.1109/nabic.2009.5393877.

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