Developing an Ai-Based Predictive Model for Early Detection of Diabetes Complications

Abstract

Globally, the number of people diagnosed with diabetes grows by the day at alarming proportions. The condition of diabetes leads to long-term complications if not detected early or if the blood sugar is not controlled properly. This research explores how to use machine learning techniques for early prediction of diabetes-related cardiovascular heart disease (CHD) and Nephropathy using relevant patient data. The paper highlights how various data preprocessing methods and feature extraction techniques, such as Oversampling using SMOTE and dimensionality reduction by PCA and LDA, impact the performance of machine learning models. The best model for predicting CHD achieved over 87% accuracy, whilst the best model for predicting Nephropathy achieved over 81% accuracy. The proposed research approach involved designing and implementing a mobile application to streamline the prediction and monitoring of diabetes-related complications. The implemented system will solve the problem of predicting diabetes-related complications, which can result in better patient outcomes, decreased costs of healthcare, and enhanced quality of life for individuals with diabetes.