Machine Learning approach for Diabetes Prediction using Pima Dataset
Authors: 
Pradnya Sumit Moon, Prachi A. Bainalwar, Shubhangi M. Borkar, Shubhangii S. ShambharkarAuthors Info & Claims
Pradnya Sumit Moon, Prachi A. Bainalwar, Shubhangi M. Borkar, Shubhangii S. ShambharkarAuthors Info & ClaimsArticle No.: 172, Pages 1 - 9
Abstract
The rising cases of diabetes globally have called for effective prediction and early detection techniques. This study explores the use of machine learning methods to identify this condition in the PIMA diabetes dataset. The research is focused on analyzing the algorithms K-Nearest Neighbors, Logistic Regression, Decision Trees, Random Forest, and XGBoost. The objective of this study is to analyze the performance of the various algorithms used in predicting diabetes. The data collected from PIMA, which consists of diagnostic and clinical measurements, is the primary source of the study. All of the algorithms are then tested and trained on this dataset to improve their precision, F1-Score, recall, and accuracy. Moreover, cross-validation and hyperparameter tuning techniques are utilized to enhance the performance of the algorithms. The findings of this study provide valuable information on the algorithms' effectiveness when it comes to identifying diabetes. Among the tested algorithms, XGBoost performed well and consistently achieved high precision, F1-Score, recall, and accuracy. It has been concluded that this algorithm is the most suitable for identifying diabetes in the PIMA dataset. Different levels of performance were exhibited by the different algorithms. This provided a comprehensive analysis of their weaknesses and strengths in predicting diabetes. The findings of this study highlight the significance of utilizing machine learning methods in predicting diabetes. It also identified XGBoost as the best performer among the evaluated systems. The findings prove valuable in helping develop effective tools for early detection of diabetes, ultimately leading to better healthcare outcomes for those at risk.
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November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
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Published: 13 May 2024
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ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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