An efficient prediction system for diabetes disease based on machine learning algorithms

Mariame Oumoulylte; Abdelkhalak Bahri; Yousef Farhaoui; Ahmad El Allaoui

doi:10.56294/dm2023173

Original

Published: 2023-12-20

DOI: https://doi.org/10.56294/dm2023173

An efficient prediction system for diabetes disease based on machine learning algorithms

Abstract

Diabetes is a persistent medical condition that arises when the pancreas loses its ability to produce insulin or when the body is unable to utilize the insulin it generates effectively. In today's world, diabetes stands as one of the most prevalent and, unfortunately, one of the deadliest diseases due to certain complications. Timely detection of diabetes plays a crucial role in facilitating its treatment and preventing the disease from advancing further. In this study, we have developed a diabetes prediction model by leveraging a variety of machine learning classification algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, and Logistic Regression, to determine which algorithm yields the most accurate predictive outcomes. we employed the famous PIMA Indians Diabetes dataset, comprising 768 instances with nine distinct feature attributes. The primary objective of this dataset is to ascertain whether a patient has diabetes based on specific diagnostic metrics included in the collection. In the process of preparing the data for analysis, we implemented a series of preprocessing steps. The evaluation of performance metrics in this study encompassed accuracy, precision, recall, and the F1 score. The results from our experiments indicate that the K-nearest neighbors’ algorithm (KNN) surpasses other algorithms in effectively differentiating between individuals with diabetes and those without in the PIMA dataset.

Keywords:

Machine Learning,

Healthcare,

Diabetes Disease,

KNN,

Naive Bayes,

SVM,

Decision Tree,

Random Forest,

Logistic Regression,

How to Cite

Oumoulylte M, Bahri A, Farhaoui Y, El Allaoui A. An efficient prediction system for diabetes disease based on machine learning algorithms. Data and Metadata [Internet]. 2023 Dec. 20 [cited 2024 Jul. 4];2:173. Available from: https://dm.saludcyt.ar/index.php/dm/article/view/173

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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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An efficient prediction system for diabetes disease based on machine learning algorithms

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