Classification

Classification Model (End to End Classification of Heart Disease - UCI Data Set)

---

Predicting Heart Disease

Create a Machine Learning Model capable of Predicting Presence of Heart Disease based on their Medical Attributes.

Problem Definition : Based on Medical Features, Predict whether the Patient have Heart Disease or not.

Data : Heart Disease UCI : ( The Orignal Data Set | Kaggle Data Set )

Features : Data Dictionary : List of Features

• Age
• Sex : 1 - Male; 0 - Female
• Chest Pain Type (4 values)
• Resting Blood Pressure
• Serum Cholestoral in mg/dl
• Fasting Blood Sugar in mg/dl
• Resting Electrocardiographic Results (values 0,1,2)
• Maximum Heart Rate Achieved : Thalach
• Exercise Induced Angina
• Oldpeak : ST Depression induced by Exercise Relative to Rest
• The Slope of the Peak Exercise ST Segment
• Number of Major Vessels (0-3) Colored by Flourosopy
• Thalassemia : 3 - Normal; 6 - Fixed Defect; 7 - Reversable Defect.
• Target : 1 - Heart Diseased; 0 - Not Heart Diseased.

---

Machine Learning Model's used for Classification.

1. Logistic Regression
2. K Nearest Neighbors
3. Random Forest Classifier

Model Selection :

1. Train Test Split
2. Cross Validation
3. Randommized Search Cross Validation
4. Grid Search Cross Validation

Classification Evaluation Metrics :

1. Accuracy Score
2. Precision Score
3. Recall Score
4. F1 Score
5. Receiver Operating Characteristics Curve
6. Area Under Curve Score
7. Classification Report

• A Model that Predicts Zero False Positive has the Precision Score of 100%

• A Model that Predicts Zero False Negative has the Recall Score of 100%

• A Model that Predicts Zero False Positive and Zero False Negative has the F1 Score of 100%

• Macro Average : Average of Precision, Recall and F1 Scores between Classes.

• Macro Average does not take Imbalanced Class.

• Weighted Average is Biased to the Class with More Samples.

Experimentation

If You have not Reached to your Expected Evaluation Metric :

1. Collect some more Data if Possible.

2. Try to Explore other Machine Learning Model.

3. Improve Current Model, Experiment with the Hyperparameters.