It's a streamlit based web app used to predict Heart Disease , Diabetes ,Parkinson's Disease

Heart Disease Prediction: Predictive models for heart disease risk assessment typically utilize various factors such as age, gender, blood pressure, cholesterol levels, smoking habits, family history, and other health-related information. These models are trained using historical data from patients with known outcomes (whether they have heart disease or not). By analyzing and learning from this data, the model can predict the probability of a person developing heart disease within a certain timeframe.

Diabetes Prediction: For diabetes prediction, machine learning models can be trained using patient data that includes features like age, weight, BMI, blood glucose levels, family history, and lifestyle factors. These models aim to predict the likelihood of a person developing diabetes or the progression of the disease based on the provided data.

Parkinson's Disease Prediction: Parkinson's disease prediction models might use features such as age, gender, medical history, genetic factors, and neurological assessments to predict the risk of Parkinson's disease or assist in early diagnosis. Machine learning algorithms can analyze patterns in the data to make predictions.