Artificial Intelligence for Early Disease Detection

Abstract

Diabetes and heart disease are the top two causes of morbidity and mortality worldwide, and while the impact of these disease is becoming overwhelming due to increased risk factors from lifestyle changes and aging populations, early detection is a critical factor in avoiding the serious complications of these diseases. The methods traditionally used to detect these diseases are not only tedious, but also expensive and often result in delayed identification. This research uses artificial intelligence (AI) and machine learning (ML) methods to create predictive frames to improve the early detection of diabetes and heart disease. Clinical parameters, such as glucose levels, blood pressure, cholesterol, and body mass index (BMI) will be analyzed using algorithms including logistic regression, random forests, and deep learning networks. The experimental evaluation shows that the models proposed in this study produce the highest accuracy, reliability, and are appropriate to be implemented by health care professionals when used in real time. Thus, this research demonstrates the utility of an AI-based system that could assist healthcare professionals with timely diagnosis, individual patient treatment plans, and improvement in patient care outcomes.