Enhancing Early Detection and Prediction of Diabetes Mellitus in Patients of Indian Origin through Rigorous Machine Learning Techniques with Comprehensive Models Evaluation

Authors

  • Prosanjeet Jyotirmay Sarkar Dr A. P. J. Abdul Kalam University, Indore, MP – 8023, INDIA
  • Santosh Pawar Dr A. P. J. Abdul Kalam University, Indore, MP – 8023, INDIA

Keywords:

Accuracy, Classification, Diabetes, Machine Learning, Model, Prediction

Abstract

Worldwide, diabetes mellitus is considered to be the 2nd deadly disease. Diabetes mellitus is a severe medical condition characterized by an abnormality in blood glucose levels resulting from pancreatic dysfunction, namely the inability to produce insulin hormones. It is a potentially fatal condition that progresses gradually and often goes unnoticed. It has a high risk for harm, malfunction, and failure of human organs like the kidneys, heart, eyes, nerves, and hypertension. There are several researches for the prediction and detection of Diabetes mellitus. The medical practitioners confirm that there is no permanent cure for diabetes mellitus; it can be kept under control by early prediction and diagnosis. The impressive establishment of a public health care infrastructure for collecting crucial and delicate data. The uses of Machine learning algorithms and numerous interesting patterns are recognized for the early prediction and detection of diseases. The current research aims to create a reliable method for detecting and predicting diabetes mellitus at an early stage by utilizing machine learning (ML) techniques. ML algorithms were performed on the Pima India Diabetes Dataset (PIDD) to develop the model. In the experiment, we employed various machine learning models, including Naïve Bayes (NB), Logistic Regression (LR), decision tree (DT), Random Forest (RF), Support Vector Machines (SVM), K-Nearest Neighbours (KNN), LightGBM (LGBM), and XGBoost (XGB), to identify cases of diabetes mellitus. Performance comparison of various ML models found that the XGBoost algorithm outperformed with an accuracy of 90.23%.

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Published

06.01.2024

How to Cite

Jyotirmay Sarkar, P. ., & Pawar, S. . (2024). Enhancing Early Detection and Prediction of Diabetes Mellitus in Patients of Indian Origin through Rigorous Machine Learning Techniques with Comprehensive Models Evaluation. International Journal of Intelligent Systems and Applications in Engineering, 12(11s), 634–639. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4484

Issue

Vol. 12 No. 11s (2024)

Section

Research Article

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