STM Journals Peer-reviewed research from Consortium e-Learning Network

Research & Reviews: A Journal of Bioinformatics Review Article

Diabetes Prediction Using ML Techniques

  1. Yash Patil Department of Computer Applications Sardar Patel Institute of Technology
  2. Shubham Gosavi Department of Computer Applications Sardar Patel Institute of Technology
  3. Nikhita Mangaonkar Department of Computer Applications Sardar Patel Institute of Technology

Abstract

Diabetes mellitus, commonly referred to as diabetes, denotes a cluster of prevalent endocrine disorders characterized by persistent elevated levels of blood sugar. Diabetes is classified into two main types: type 1 and type 2. Type 1 diabetes arises when the body is unable to produce insulin, while type 2 diabetes involves either insulin resistance or insufficient insulin production. Early detection and intervention are essential to reduce its harmful impacts. The study delves into the efficiency of diverse ML algorithms in anticipating the emergence of diabetes by considering demographic, clinical, and lifestyle elements. The study utilises a comprehensive dataset comprising health records of individuals, encompassing parameters such as age, body mass index (BMI), glucose levels, insulin sensitivity, family history, and lifestyle habits. Feature selection plays a crucial role in diabetes research by identifying the key variables that contribute significantly to the development and management of the condition. These techniques help researchers and healthcare professionals focus on the most relevant factors. Various machine learning algorithms are used to forecast outcomes, such as the onset of diabetes and other health conditions. Logistic regression, known for its simplicity and interpretability, is compared against more complex models like random forests, decision trees, and support vector machines. Evaluation of these models relies on performance metrics such as accuracy, sensitivity, specificity, and AUC-ROC, which help determine their appropriateness for various prediction tasks.

Keywords

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