The binary classification machine learning model to predict the probability of a person having a heart disease based on key personal indicators.
The model uses a random forest classifier algorithm, which is a decision tree-based approach that provides high accuracy for binary classification problems.
The model takes in various personal indicators such as age, sex, blood pressure, cholesterol levels, and smoking habits as input features, and predicts the probability of the person having a heart disease as output. Pre-processing is involved in cleaning the data, handling missing values, and normalizing the input features, which enhances the accuracy of the model.
The final solution is deployed in a Python web server, making it accessible to users. The web application takes in the input features from the user and returns the predicted probability of having a heart disease. This solution provides a fast and accurate tool for screening individuals for heart disease, enabling early detection and treatment.
Overall, this binary classification machine learning model is a powerful tool that uses random forest classifier algorithm and pre-processing to accurately predict the probability of a person having a heart disease based on their personal indicators. The deployment of the solution in a Python web server ensures its accessibility and user-friendliness.