Identification and Forecast of Heart and Diabetic Disease Using Machine Learning

Heart disease and diabetes are two of the most prevalent and costly chronic diseases in the world. Early detection and management of these conditions are critical in preventing their progression and reducing the risk of complications, morbidity, mortality, and healthcare costs. However, identifying individuals at risk of developing heart disease and diabetes is challenging, as they often present with non-specific symptoms, and risk factors may go unnoticed until the disease is advanced. Additionally, healthcare resources are often limited, and healthcare providers may not have the time or expertise to screen all patients for these diseases. Therefore, there is a need to develop accurate predictive models using machine learning (ML) methods for heart disease and diabetes that can identify individuals at risk of developing these diseases early on. Such models can help healthcare providers prioritize patients for screening and intervention, thus improving patient outcomes and reducing the burden on healthcare systems. This paper provides a detailed overview of the ML models and their performance evaluation. The results indicate that the model can accurately predict the likelihood of an individual having diabetes and heart disease, with a high degree of precision.