Heart Disease & Diabetes Prediction Using Machine Learning

In the contemporary age of digital healthcare, the prompt identification and forecasting of chronic illnesses such as diabetes are vital in averting severe complications and promoting an enhanced quality of life. Diabetes, recognized as among the most prevalent non-communicable diseases worldwide, impacts millions, with numerous instances remaining undiagnosed due to insufficient access to preventive diagnostics. While traditional medical diagnosis is effective, it tends to be time-intensive and often reactive instead of proactive. To tackle this issue, there is an increasing demand for intelligent, data-driven systems capable of accurately forecasting diabetes’s on set at an early stage. This project presents a Diabetes Prediction System that applies Machine Learning and a Ensemble Technique, aimed at predicting an individual’s diabetic status based on clinical and lifestyle factors. The system utilizes patient health datasets that encompass features such as glucose levels, BMI, age, insulin levels, blood pressure, and family medical history. Initially, the dataset undergoes preprocessing and cleaning to address anomalies. Subsequently, feature selection and transformation techniques are employed to enhance model accuracy and reduce noise