Heart disease detection involves several tests and evaluations to diagnose conditions affecting the heart. Here are some key points:
Symptoms: Shortness of breath, chest pain, fainting, slow or fast heartbeat, swelling in the legs.
Functionality:
1. Data Collection: Gather patient data including age, gender, blood pressure, cholesterol levels, and other relevant factors.
2. Feature Selection: Identify the most relevant features that contribute to heart disease.
3. Model Training: Use machine learning algorithms like Logistic Regression, Decision Trees, Random Forest, and Support Vector Machines to train the model on the collected data.
4. Prediction: The trained model can predict the probability of heart disease based on new patient data.
Technology Stack: Assess the model's accuracy using metrics like precision, recall, and F1-score, Machine learning model.
Benefits: Machine learning for heart disease detection enables early diagnosis, personalized treatment, high accuracy, efficiency, and cost-effective healthcare solutions.
