Editorial
Modelling of Diabetic Cases for Effective Prevalence Classification
@ARTICLE{10.4108/eetpht.10.5514, author={Shrey Shah and Nonita Sharma and Tanupriya Choudhury and Maganti Syamala}, title={Modelling of Diabetic Cases for Effective Prevalence Classification}, journal={EAI Endorsed Transactions on Pervasive Health and Technology}, volume={10}, number={1}, publisher={EAI}, journal_a={PHAT}, year={2024}, month={3}, keywords={Machine Learning, Ensemble Model, Diabetes Protection, Healthcare, Accuracy, Sensitivity, Specificity}, doi={10.4108/eetpht.10.5514} }- Shrey Shah
Nonita Sharma
Tanupriya Choudhury
Maganti Syamala
Year: 2024
Modelling of Diabetic Cases for Effective Prevalence Classification
PHAT
EAI
DOI: 10.4108/eetpht.10.5514
Abstract
INTRODUCTION: This study compares and contrasts various machine learning algorithms for predicting diabetes. The study of current research work is to analyse the effectiveness of various machine learning algorithms for diabetes prediction. OBJECTIVES: To compare the efficacy of various machine learning algorithms for diabetic prediction. METHODS: For the same, a diabetic dataset was subjected to the application of various well-known machine learning algorithms. Unbalanced data was handled by pre-processing the dataset. The models were subsequently trained and assessed using different performance metrics namely F1-score, accuracy, sensitivity, and specificity. RESULTS: The experimental results show that the Decision Tree and ensemble model outperforms all other comparative models in terms of accuracy and other evaluation metrics. CONCLUSION: This study can help healthcare practitioners and researchers to choose the best machine learning model for diabetes prediction based on their specific needs and available data.
Copyright © 2024 S. Shah et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
