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Relevant bibliographies by topics / Clustering Healthcare data Silhouette score value K-means DBSCAN

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Academic literature 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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Journal articles on the topic "Clustering Healthcare data Silhouette score value K-means DBSCAN"

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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3

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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5

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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6

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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7

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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