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Academic literature on the topic 'PIMA Indian data-set'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "PIMA Indian data-set"

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Rajni and Amandeep. "RB-bayes algorithm for the prediction of diabetic in "PIMA Indian dataset"." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 6 (2019): 4866–72. https://doi.org/10.11591/ijece.v9i6.pp4866-4872.

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Diabetes is a major concern all over the world. It is increasing at a fast pace. People can avoid diabetes at an early stage without any test. The goal of this paper is to predict the probability of whether the person has a risk of diabetes or not at an early stage. This would lead to having a great impact on their quality of human life. The datasets are Pima Indians diabetes and Cleveland coronary illness and consist of 768 records. Though there are a number of solutions available for information extraction from a huge datasets and to predict the possibility of having diabetes, but the accura
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MANASA, BYAGRI. "Prediction of Diabetes in Women." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49629.

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Abstract: Nowadays, diabetes is a common disease that affects millions of people all over the world, and women are mostly affected by this disease. Recent healthcare studies have applied various innovative and advanced technologies to diagnose people and predict their disease based on clinical data. One of such technology is machine learning (ML) in which diagnosis and prediction can be made more accurately. In this paper, the designed model predicts the diabetes of females of Pima Indian heritage by taking the clinical data-set. Here, this problemis considered a binary classification problem.
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Laman R. Sultan. "Diagnosis of Type II Diabetes Based on Feed forward Neural Network Techniques." International Journal of Research in Pharmaceutical Sciences 11, no. 1 (2020): 1109–16. http://dx.doi.org/10.26452/ijrps.v11i1.1943.

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Diabetes is a disease caused by an increase in blood glucose levels due to insulin secretion deficiency (type I diabetes) or impaired insulin activity (type II diabetes). More than 90% of people with this condition are diagnosed with type II diabetes. Due to the sharply prevalence of type 2 diabetes in recent years, the prognosis and early diagnosis of the disease have become even more important. In this study, a model for diagnosis of type II diabetes was developed using Artificial Neural Network (ANN) method. The execution of the Frequent Pattern Growth algorithm on medical data is difficult
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Balhara, Shreyansh. "DEVELOPING A DATA MINING BASED EFFICACIOUS PREDICTION MODEL OF DIABETICS AND AILED AILMENTS." International Journal of Research in Medical Sciences and Technology 11, no. 01 (2022): 216–21. http://dx.doi.org/10.37648/ijrmst.v11i01.021.

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This model of recovering useful data and models from the information is called KDD (Knowledge Discovery of Database), which includes specific steps like information finding, grouping and change review. AI analyses are called managed and independently. A supervised learning analysis uses insight to predict new or unseeable information, though unaided measures can draw impedances from informative clusters. Supervised learning is additionally described as arrangement. This review uses clustering methods to deliver a more precise area with the class. The clustering analyses have been applied to th
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Ray, Kumar S. "Pattern Recognition Based on Fuzzy Set and Genetic Algorithm." International Journal of Image and Graphics 14, no. 03 (2014): 1450009. http://dx.doi.org/10.1142/s0219467814500090.

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In this paper, we consider a soft computing approach to pattern classification. Our basic tools for soft computing are fuzzy relational calculus (FRC) and genetic algorithm (GA). We introduce a new interpretation of multidimensional fuzzy implication (MFI) to represent our knowledge about the training data set. We also consider the notion of a fuzzy pattern vector to handle the fuzzy information granules of the quantized pattern space and to represent a population of training patterns in the quantized pattern space. The construction of the pattern classifier is essentially based on the estimat
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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 ne
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Li, Yuchen. "Research on the Influencing Factors that affecting Female Diabetes." Highlights in Science, Engineering and Technology 91 (April 15, 2024): 304–9. http://dx.doi.org/10.54097/adfmaq54.

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This paper provides an in-depth look at diabetes as a growing public health problem among women in light of globalization and lifestyle changes. Diabetes mellitus encompasses a collection of metabolic conditions marked by elevated blood glucose levels following a fasting period, typically resulting from inadequate insulin function or reduced sensitivity to insulin within the body. As per the International Diabetes Federation (IDF), the projected global population living with diabetes in 2011 was approximately 366 million. The IDF's projections indicate a potential increase to 522 million by 20
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Li, Liping. "Comparative Research on Diabetes Influencing Factors Based on Random Forest and Decision Tree Models." Highlights in Science, Engineering and Technology 72 (December 15, 2023): 231–42. http://dx.doi.org/10.54097/7m4x7j04.

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In tandem with society's rapid progress, the prevalence of diabetes has risen sharply due to factors such as changes in eating habits, bad lifestyle and serious aging problems. Therefore, it is of great significance to forecast the influencing factors of diabetes mellitus. In this paper, the Pima Indian Diabetes data set in UCI is taken as the experimental data, the Random Forest and Decision Tree methods are used for modeling. The effect of these two models is analyzed according to four indicators, comprised of rate of accuracy, ratio of precision, rate of recall and F1-score. The F1-score ra
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9

Raghavendra, S., and Kumar J. Santosh. "Performance evaluation of random forest with feature selection methods in prediction of diabetes." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (2020): 353–59. https://doi.org/10.11591/ijece.v10i1.pp353-359.

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Data mining is nothing but the process of viewing data in different angle and compiling it into appropriate information. Recent improvements in the area of data mining and machine learning have empowered the research in biomedical field to improve the condition of general health care. Since the wrong classification may lead to poor prediction, there is a need to perform the better classification which further improves the prediction rate of the medical datasets. When medical data mining is applied on the medical datasets the important and difficult challenges are the classification and predict
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Bukhari, Muhammad Mazhar, Bader Fahad Alkhamees, Saddam Hussain, Abdu Gumaei, Adel Assiri, and Syed Sajid Ullah. "An Improved Artificial Neural Network Model for Effective Diabetes Prediction." Complexity 2021 (April 21, 2021): 1–10. http://dx.doi.org/10.1155/2021/5525271.

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Data analytics, machine intelligence, and other cognitive algorithms have been employed in predicting various types of diseases in health care. The revolution of artificial neural networks (ANNs) in the medical discipline emerged for data-driven applications, particularly in the healthcare domain. It ranges from diagnosis of various diseases, medical image processing, decision support system (DSS), and disease prediction. The intention of conducting the research is to ascertain the impact of parameters on diabetes data to predict whether a particular patient has a disease or not. This paper de
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Book chapters on the topic "PIMA Indian data-set"

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Kumari, Ashwini, Naveena, and Manjula. "Diabetes Prediction Using Machine Learning Technique in Health-Care Applications." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3373-0370-3.ch012.

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Diabetes is a condition that makes blood sugar levels soar. When glucose levels go up, this puts a lot of pressure on the organs like the kidneys and the cardiovascular system that can become life-threatening. Using the PIMA Indian Diabetes Dataset, this study compares several machine learning models, including Random Forest, Logistic Regression, Decision Tree, Naïve Bayes, and Support Vector Machines (SVM), for predicting diabetes in females. Initially, the model training and evaluation were conducted using all nine data-set features. Evaluation metrics like accuracy, recall & precision w
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Reports on the topic "PIMA Indian data-set"

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Glewwe, Paul, Zoe James, Jongwook Lee, Caine Rolleston, and Khoa Vu. What Explains Vietnam’s Exceptional Performance in Education Relative to Other Countries? Analysis of the Young Lives Data from Ethiopia, Peru, India and Vietnam. Research on Improving Systems of Education (RISE), 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/078.

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Vietnam’s strong performance on the 2012 and 2015 PISA assessments has led to interest in what explains the strong academic performance of Vietnamese students. Analysis of the PISA data has not shed much light on this issue. This paper analyses a much richer data set, the Young Lives data for Ethiopia, India (Andhra Pradesh and Telangana), Peru and Vietnam, to investigate the reasons for the strong academic performance of 15-year-olds in Vietnam. Differences in observed child and household characteristics explain 37-39% of the gap between Vietnam and Ethiopia, while observed school variables e
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