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Journal articles on the topic 'Clustering Healthcare data Silhouette score value K-means DBSCAN'

Author: Grafiati

Published: 3 June 2025

Last updated: 1 August 2025

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1

Godwin, Ogbuabor, and F. N. Ugwoke. "Clustering Algorithm for a Healthcare Dataset Using Silhouette Score Value." International Journal of Computer Science & Information Technology (IJCSIT) 10, no. 2 (2018): 27–37. https://doi.org/10.5281/zenodo.1248795.

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The huge amount of healthcare data, coupled with the need for data analysis tools has made data mining interesting research areas. Data mining tools and techniques help to discover and understand hidden patterns in a dataset which may not be possible by mainly visualization of the data. Selecting appropriate clustering method and optimal number of clusters in healthcare data can be confusing and difficult most times. Presently, a large number of clustering algorithms are available for clustering healthcare data, but it is very difficult for people with little knowledge of data mining to choose

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2

Frangky, Frangky, Rudolf Sinaga, and M. Raihansyah. "Analisis Segmentasi Pasien Berdasarkan Persepsi Kualitas Pelayanan dengan Algoritma Clustering." Explorer 5, no. 1 (2025): 52–58. https://doi.org/10.47065/explorer.v5i1.1818.

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Patient segmentation based on perceptions of service quality is a crucial step in improving patient experiences, optimizing resources, and enhancing healthcare service quality. However, understanding patients' needs and priorities in depth poses a challenge, particularly for hospitals serving populations with diverse demographic backgrounds. This study aims to cluster patients in a private hospital in Jambi City based on their perceptions of service quality using the K-Means algorithm. Data were collected from a 2022-2023 survey, covering patient demographics and perceptions of service quality

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Dharmawan, Tio, Chinta 'Aliyyah Candramaya, and Vandha Pradwiyasma Widharta. "Forming Dataset of The Undergraduate Thesis using Simple Clustering Methods." International Journal of Innovation in Enterprise System 7, no. 01 (2023): 31–40. http://dx.doi.org/10.25124/ijies.v7i01.187.

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Each university collects many undergraduate theses data but has yet to process it to make it easier for students to find references as desired. This study aims to classify and compare the grouping of documents using expert and simple clustering methods. Experts have done ground truth using OR Boolean Retrieval and keyword generation. The best cluster was discovered by the experiments using the K-Means, K-Medoids, and DBSCAN clustering methods and using Euclidean, Manhattan, City Block, and Cosine Similarity metrics. The cluster with the best Silhouette Score compared to the accuracy of the cat

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4

Choque-Soto, Vanessa Maribel, Victor Dario Sosa-Jauregui, and Waldo Ibarra. "Characterization of the Dropout Student Profile Using Data Mining Techniques." Revista de Gestão Social e Ambiental 19, no. 2 (2025): e011306. https://doi.org/10.24857/rgsa.v19n2-067.

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Objective: One of the primary concerns in Educational Data Mining is student dropout rates. This study aims to investigate student dropout rates in higher education by identifying and analyzing the demographic and academic characteristics of university students who discontinue their studies. Theoretical Framework: Based on Educational Data Mining with clustering techniques, this study utilizes pattern recognition and data segmentation models to analyze dropout behavior within Informatics programs. Method: Data mining techniques were applied to a dataset that contained demographic and academic

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Mutawalli, Lalu, Sofiansyah Fadli, and Supardianto Supardianto. "Komparasi Metode Perhitungan Jarak K-Means Paling Baik Terhadap Pembentukan Pola Kunjungan Wisatawan Mancanegara." Journal of Information System Research (JOSH) 5, no. 1 (2023): 159–66. http://dx.doi.org/10.47065/josh.v5i1.4377.

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Understanding patterns among foreign tourists is an urgent matter. These patterns can become knowledge that helps in making better decisions because they are data-driven. The pattern to be elaborated on is regarding the clustering of visits by foreign tourists to tourist destinations in Jakarta. Data mining is an approach that extracts knowledge patterns from a dataset. K-Means is one of the data mining algorithms used for clustering data, where data is grouped based on similarity in features and attributes. This study compares the Euclidean Distance, Manhattan Distance, and Haversine Distance

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Bateja, Ritika, Sanjay Kumar Dubey, and Ashutosh Kumar Bhatt. "Diabetes Prediction and Recommendation Model Using Machine Learning Techniques and MapReduce." Indian Journal Of Science And Technology 17, no. 26 (2024): 2747–53. http://dx.doi.org/10.17485/ijst/v17i26.530.

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Objectives: To deliver patient centric healthcare for diabetic patients using a fast and efficient diabetic prediction and recommendation model which will not only help in early diagnoses of disease but also recommend appropriate medicine for controlling it at stage 1. Methods: The Support Vector Machine Classifier is further enhanced with Particle Swarm Optimization (PSO) and used for the prediction of diabetes. Collaborative Filtering is used for drug recommendation, which produces a suitable list of medications that correspond to the diagnoses of diabetes patients. Improved Density-Based Sp

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Samsudin, Angga Radlisa, Dhomas Hatta Fudholi, and Lizda Iswari. "TEMPORAL SPATIAL PROPERTY PROFILING AND IDENTIFICATION OF EARTHQUAKE PRONE AREAS USING ST-DBSCAN AND K-MEANS CLUSTERING." Jurnal Teknik Informatika (Jutif) 5, no. 3 (2024): 917–29. https://doi.org/10.52436/1.jutif.2024.5.3.1293.

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Indonesia is a country located at the confluence of three major tectonic plates, namely Indo-Australia, Eurasia, and the Pacific so that earthquakes often occur, one of which is in West Nusa Tenggara Province. One way to accelerate the disaster mitigation process is to analyze earthquake occurrence based on spatial temporal aspects. This study uses data from BMKG NTB Province during 2018 with a total of 3,699 earthquake events which are then analyzed using ST-DBSCAN and K-Means. ST-DBSCAN analysis was used to determine earthquake prone areas based on the date and location of the event, while k

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8

Ramadhan, Hafid, Mohammad Rizal Abdan Kamaludin, Muhammad Alfan Nasrullah, and Dwi Rolliawati. "Comparison of Hierarchical, K-Means and DBSCAN Clustering Methods for Credit Card Customer Segmentation Analysis Based on Expenditure Level." Journal of Applied Informatics and Computing 7, no. 2 (2023): 246–51. http://dx.doi.org/10.30871/jaic.v7i2.5790.

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The amount of data from credit card users is increasing from year to year. Credit cards are an important need for people to make payments. The increasing number of credit card users is because it is considered more effective and efficient. The third method used today has a function to determine the effective outcome of credit card user scenarios. In this study, a comparison was made using the Hierarchical Clustering, K-Means and DBSCAN methods to determine the results of credit card customer segmentation analysis to be used as a market strategy. The results obtained based on the best silhouett

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9

Ritika, Bateja, Kumar Dubey Sanjay, and Kumar Bhatt Ashutosh. "Diabetes Prediction and Recommendation Model Using Machine Learning Techniques and MapReduce." Indian Journal of Science and Technology 17, no. 26 (2024): 2747–53. https://doi.org/10.17485/IJST/v17i26.530.

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Abstract <strong>Objectives:</strong> To deliver patient centric healthcare for diabetic patients using a fast and efficient diabetic prediction and recommendation model which will not only help in early diagnoses of disease but also recommend appropriate medicine for controlling it at stage 1. <strong>Methods:</strong> The Support Vector Machine Classifier is further enhanced with Particle Swarm Optimization (PSO) and used for the prediction of diabetes. Collaborative Filtering is used for drug recommendation, which produces a suitable list of medications that correspond to the

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10

Husna, Farida Amila, Diana Purwitasari, Bayu Adjie Sidharta, Drigo Alexander Sihombing, Amiq Fahmi, and Mauridhi Hery Purnomo. "A Clustering Approach for Mapping Dengue Contingency Plan." Scientific Journal of Informatics 9, no. 2 (2022): 149–60. http://dx.doi.org/10.15294/sji.v9i2.36885.

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Purpose: The dengue epidemic has an increasing number of sufferers and spreading areas along with increased mobility and population density. Therefore, it is necessary to control and prevent Dengue Hemorrhagic Fever (DHF) by mapping a DHF contingency plan. However, mapping a dengue contingency plan is not easy because clinical and managerial issues, vector control, preventive measures, and surveillance must be considered. This work introduces a cluster-based dengue contingency planning method by grouping patient cases according to their environment and demographics, then mapping out a plan and

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11

Iqbal Prayoga Willyana, Asep Id Hadiana, and Ridwan Ilyas. "Analisis Klaster Daerah Rawan Gempa di Indonesia Menggunakan K-Means dan DBSCAN Berbasis Data Historis BMKG." TEMATIK 12, no. 1 (2025): 59–71. https://doi.org/10.38204/tematik.v12i1.2369.

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Indonesia is one of the countries with the highest levels of seismic activity in the world because it is located at the meeting point of three major plates. The high potential for earthquakes requires a data-based approach to map vulnerable areas more accurately. This study aims to group earthquake-prone areas in Indonesia using the K-Means and DBSCAN clustering algorithms. The dataset used includes spatial data (latitude, longitude) and seismic data (magnitude, depth, phasecount, azimuth_gap) obtained from the BMKG earthquake catalog for the period 2008–2025. The study begins with the data pr

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12

Abdikerimova, Gulzira, Dana Khamitova, Akmaral Kassymova, et al. "Development of a Model for Soil Salinity Segmentation Based on Remote Sensing Data and Climate Parameters." Algorithms 18, no. 5 (2025): 285. https://doi.org/10.3390/a18050285.

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The paper presents a hybrid machine learning model for the spatial segmentation of soils by salinity using multispectral satellite data from Sentinel-2 and climate parameters of the ERA5-Land model. The proposed method aims to solve the problem of accurate soil cover segmentation under climate change and high spatial heterogeneity of data. The approach includes the sequential application of unsupervised learning algorithms (K-Means, hierarchical clustering, DBSCAN), the XGBoost model, and a multitasking neural network that performs simultaneous classification and regression. At the first stage

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13

Zhukabayeva, Tamara, Zulfiqar Ahmad, Aigul Adamova, Nurdaulet Karabayev, and Assel Abdildayeva. "An Edge-Computing-Based Integrated Framework for Network Traffic Analysis and Intrusion Detection to Enhance Cyber–Physical System Security in Industrial IoT." Sensors 25, no. 8 (2025): 2395. https://doi.org/10.3390/s25082395.

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Industrial Internet of things (IIoT) environments need to implement reliable security measures because of the growth in network traffic and overall connectivity. Accordingly, this work provides the architecture of network traffic analysis and the detection of intrusions in a network with the help of edge computing and using machine-learning methods. The study uses k-means and DBSCAN techniques to examine the flow of traffic in a network and to discover several groups of behavior and possible anomalies. An assessment of the two clustering methods shows that K-means achieves a silhouette score o

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14

Fitriyani, Rofi, Ayip Luthfi Firmansyah, Al Yaafi Nadiyal Fithri, and Larasati Angelica Nurfadillah. "Penerapan Algoritma Clustering untuk Segmentasi Pelanggan E-commerce berdasarkan Data Pembelian dan Aktivitas." SEMINAR TEKNOLOGI MAJALENGKA (STIMA) 8 (October 1, 2024): 372–79. http://dx.doi.org/10.31949/stima.v8i0.1129.

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In the current digital era, e-commerce has become one of the main pillars of global trade. With the ever-increasing amount of transaction and user activity data, e-commerce companies are faced with the challenge of understanding and managing diverse customer segments more effectively. This paper discusses the application of clustering algorithms for e-commerce customer segmentation based on purchasing data and user activity. The aim of this research is to identify homogeneous customer groups to support more targeted marketing strategies and increase customer retention. The problem faced is how

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15

Godwin, Ogbuabor. "CLUSTERING ALGORITHM FOR A HEALTHCARE DATASET USING SILHOUETTE SCORE VALUE." October 19, 2018. https://doi.org/10.5281/zenodo.1466257.

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The huge amount of healthcare data, coupled with the need for data analysis tools has made data mining interesting research areas. Data mining tools and techniques help to discover and understand hidden patterns in a dataset which may not be possible by mainly visualization of the data. Selecting appropriate clustering method and optimal number of clusters in healthcare data can be confusing and difficult most times. Presently, a large number of clustering algorithms are available for clustering healthcare data, but it is very difficult for people with little knowledge of data mining to choose

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16

"Comparative Analysis of Clustering Algorithms: Performance Evaluation Using the Weighted Product Method (WPM)." Computer Science, Engineering and Technology 2, no. 4 (2025): 34–42. https://doi.org/10.46632/cset/2/4/5.

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Introduction: Clustering algorithms play a key role in grouping data objects based on their similarities. A popular method, K-means, works by repeatedly adjusting the center of each cluster until convergence is achieved. This method, especially in the PAM form, is widely used in clustering analysis for its effectiveness in separating data. Clustering, an unsupervised learning technique, is very effective in discovering hidden patterns within datasets. Clustering focuses on dividing data into meaningful groups, rather than using predefined labels, as supervised algorithms do. By finding underly

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17

-, Srilekha S., Priyadharshini P. -, and Adhilakshmi M. -. "Comparative Evaluation of K-Means, Hierarchical Clustering, and DBSCAN in Blood Donor Segmentation." International Journal For Multidisciplinary Research 6, no. 4 (2024). http://dx.doi.org/10.36948/ijfmr.2024.v06i04.26755.

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Clustering techniques are pivotal in the fields of data analysis and pattern recognition, offering significant insights by grouping data points with similar characteristics. This study aims to perform a comprehensive comparison of three widely used clustering algorithms—K-Means, Hierarchical Clustering, and DBSCAN—on a dataset of blood donors. The objective is to determine which algorithm achieves the most precise and effective clustering of the data, taking into account factors such as donor location, blood type, and donation frequency. The study presents a novel approach by integrating a web

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18

Amol Bhopale, Sanskar Zanwar, Aarya Balpande, and Jaweria Kazi. "Optimised Cluster-based Approach for Healthcare Data Analytics." International Journal of Next-Generation Computing, February 15, 2023. http://dx.doi.org/10.47164/ijngc.v14i1.1011.

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Data analytics is an intriguing study due to the fact that an enormous volume of healthcare data is being generated by different smart IOT-based health tracking devices, and the Artificial Intelligent-based applications. Data analytic tools and unsupervised techniques combinedly make it possible to find and comprehend hidden patterns in a dataset that may not be visible through simple data display. Grouping of voluminous data objects into homogenous clusters is a crucial operation in soft computing. Choosing the right clustering technique and the correct number of partitions to divide the heal

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Amit, Sajid, Abdulla Al Kafy, Mushfiqur Rahman, and Iftakhar Ahmed. "Youth Capability Ecosystems and Strategic Business Models: Leveraging Market Segmentation for Sustainable Development in Emerging Economies." Business Strategy & Development 8, no. 2 (2025). https://doi.org/10.1002/bsd2.70137.

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ABSTRACTAs businesses increasingly seek to align commercial strategies with development goals in emerging markets, understanding the complex youth capability landscape becomes crucial for sustainable growth and impact. This research examines how businesses can align commercial strategies with sustainable development goals by understanding youth capability ecosystems in emerging markets, focusing on Bangladesh as a representative case. Through a mixed‐methods approach with advanced data science techniques, we analyzed survey data from 400 youth respondents in Dhaka to identify distinct market s

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