Care_bot

An Machine Learning model that can predict whether the user is likely to develop any heart disease or not in the next decade and provides some health tips to improve the health condition of the user.

Machine Learning Model:

1. Model Type: Utilizes a Random Forest Classifier, a popular ensemble learning method for classification tasks.
2. Training Data: The model is trained on a dataset containing a variety of health-related features such as age, sex, smoking status, blood pressure, cholesterol levels, etc., along with labels indicating whether or not an individual developed a heart disease within the following decade.
3. Preprocessing: Before training, the data is preprocessed to handle missing values, normalize numerical features, and encode categorical variables.
4. Training Process: The Random Forest model is trained on the preprocessed dataset, leveraging the ensemble of decision trees to make predictions.
5. Model Evaluation: The model's performance is evaluated using metrics such as accuracy, precision, recall, and F1-score to assess its effectiveness in predicting heart disease.

Tkinter GUI Application:

1. User Interface: The GUI provides an intuitive interface for users to input their health-related information, including demographic details, lifestyle habits, and health metrics.
2. Input Validation: Incorporates input validation to ensure users provide valid data, preventing errors and ensuring the accuracy of predictions.
3. Prediction Output: Upon submitting the input data, the application invokes the trained Random Forest model to predict the likelihood of the user developing a heart disease in the next decade.
4. Result Presentation: Displays the prediction result to the user, indicating whether they are at risk of developing a heart disease or not.
5. Health Tips: Offers personalized health tips based on the user's input data and predicted risk level. These tips address specific health factors such as high cholesterol, abnormal blood pressure, elevated heart rate, and glucose levels, aiming to improve the user's overall health condition.
6. User Feedback: Allows users to receive actionable insights and recommendations, empowering them to make informed decisions about their health and lifestyle choices.

By combining machine learning with a user-friendly GUI application, this solution not only predicts the risk of heart disease but also provides valuable insights and guidance for promoting better health outcomes.