Health Data Analysis for In-Depth Understanding of Patterns, Prediction, and Disease Management: A Case Study on Diabetes Mellitus

Abstract

The present study aims to address the intricate nature of diabetes mellitus by employing data analysis to gain profound insights into individual health patterns, predict risks of complications, and formulate personalized solutions for disease management. Data were sourced from diverse repositories, including the UCI Machine Learning Repository, Kaggle, and Data.gov, encompassing medical records, laboratory histories, and lifestyle data of diabetes patients. Preprocessing involved outlier detection, normalization, and handling data imbalances using the Synthetic Minority Over-sampling Technique (SMOTE). Principal Component Analysis (PCA) was utilized for feature extraction to facilitate a comprehensive understanding of health patterns. Predictive models, namely Random Forest, Support Vector Machine, and Neural Network, underwent rigorous training and validation. Concurrently, disease management solutions were crafted based on model recommendations. Research findings demonstrated commendable performance, particularly with the Neural Network model achieving an AUC-ROC of 0.92. This study's contribution is anticipated to usher in novel approaches in chronic disease management, particularly diabetes, by applying data science principles to enhance comprehension, prediction, and disease management, potentially elevating the quality of life for patients.