HealthAI: An Intelligent Machine Learning Approach to Early Prediction of Diabetes Risk

Abstract

Abstract
Diabetes is a persistent disease that needs to be diagnosed early and effectively managed to prevent the emergence of any further complications. In most instances, individuals may not be aware of their risk factors of diabetes until their symptoms become evident.This creates a need for systems that can examine commonly available health information and provide an early indication of possible diabetes risk. Machine learning offers a practical approach for analysing multiple patient-related factors and identifying patterns that may be difficult to assess manually.
The current paper describes HealthAI - an intelligent machine learning approach that can be used for predicting the risk of developing diabetes at the early stages and for decision support in healthcare. Clinical and demographic features, including patient's age, body mass index, glucose level, related to blood pressure, level of HbA1c, heart diseases, smoking habits, and other features of the health condition are taken into account. The proposed model structure includes data pre-processing, feature selection, modeling, prediction, and evaluation steps. Different classification algorithms like Logistic Regression, Decision Tree, Random Forest, SVM, K-Nearest Neighbor, and Naïve Bayes can be used. The performance of the model could be evaluated based on accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrix.
HealthAI model could provide early information regarding the risk that will allow preventing diabetes. It is important to mention that the suggested solution cannot be considered as a diagnostic tool, but rather it serves as an assistance for making decisions.