Diagnosing Diabetes Onset with Machine Learning Enhanced Predictive Analysis in Pima Indians
Diagnosing Diabetes Onset with Machine Learning Enhanced Predictive Analysis in Pima Indians |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-8 |
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| Year of Publication : 2025 | ||
| Author : Abhishek Kumar, Partha Sarathi Bishnu, Pushpanjali R. Ojha | ||
| DOI : 10.14445/22315381/IJETT-V73I8P127 | ||
How to Cite?
Abhishek Kumar, Partha Sarathi Bishnu, Pushpanjali R. Ojha,"Diagnosing Diabetes Onset with Machine Learning Enhanced Predictive Analysis in Pima Indians", International Journal of Engineering Trends and Technology, vol. 73, no. 8, pp.312-332, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I8P127
Abstract
Since diabetes is becoming more common, early and precise prediction is essential for prevention and management. This study predicts diabetes mellitus in the Pima Indian community using Deep Learning (DL) and advanced Machine Learning (ML) techniques. Model performance improved significantly through meticulous data preprocessing, incorporating imputation for missing values, data balancing techniques, advanced feature engineering, and sophisticated statistical methods for managing incomplete data and identifying key features. The models employed here include a multilayer deep learning model called the Tree-based Pipeline Optimization Tool (TPOT) and ensemble learning using LightGBM and K-Nearest Neighbors (KNN). High accuracy score, precision score, recall score, and F1 score of roughly 94.5% were attained by each model following a rigorous review and improvement procedure. Comprehensive experiments were conducted, with results analyzed graphically and numerically, offering in-depth insights and recommendations. The proposed approach outperforms the most advanced techniques already in use, proving its efficacy and emphasizing the critical role that prompt and precise prediction plays in the prevention and treatment of diabetes in high-risk populations.
Keywords
Deep Learning, Diabetes prediction, Feature engineering, Machine Learning, Medical informatics.
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