The goal of this research is to use machine learning and health indicator data to create a prediction model for diabetes diagnosis. The study finds important patterns linked to diabetes by examining a data set that includes a variety of health metrics, including BMI, blood pressure, cholesterol levels, lifestyle factors (such as smoking, physical activity, and food), and socioeconomic characteristics (such as income and education). To guarantee model robustness, the data pipeline include prepossessing procedures including feature scaling, encoding, and managing class imbalances. The model is constructed using sophisticated algorithms like Random Forest and logistic regression which are assessed using measures like accuracy, recall. With its effective, data-driven approach to early diabetes identification, this technology enhances preventive care tactics for at-risk groups and gives medical professionals the ability to make well-informed judgments.
