Heart Disease

• This notebook looks into using various Python-based machine learning and data science libraries in an attempt to build a machine learning model capable of predicting whether or not someone has heart disease based on their medical attributes.

• Original Data -- https://archive.ics.uci.edu/ml/datasets/heart+Disease

• Kaggle Data -- https://www.kaggle.com/ronitf/heart-disease-uci

• We're going to take the following approach:

1. Problem definition
2. Data
3. Evaluation
4. Features
5. Modelling
6. Experimentation

AIM

To find whether the person is having heart disease or not.

Resource Used

• Python : v3.8 Jupyter Notebook : v6.4
• Packages : Pandas : v1.4.3, Numpy : v1.23.0, Matplotlib : v3.5.2, Matplotlib-inline : v0.1.3, Seaborn : v0.10.1, Scikit-Learn : v1.1.1
• Different Machine Learning Model such as RandomForestClassifier, LogisticRegression, KNeighborsClassifier used to get the high Accuracy Score and also Hyperparameter tuning is done to get the best result using RandomizedSearchCV and GridSearchCV.

EDA (Exploratory Data Analysis)

• There were no null values in the dataset so EDA was done directly.

1. age - age in years
2. sex - (1 = male; 0 = female)
3. cp - chest pain type 0: Typical angina: chest pain related decrease blood supply to the heart 1: Atypical angina: chest pain not related to heart 2: Non-anginal pain: typically esophageal spasms (non heart related) 3: Asymptomatic: chest pain not showing signs of disease
4. trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern
5. chol - serum cholestoral in mg/dl serum = LDL + HDL + .2 * triglycerides above 200 is cause for concern
6. fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) '>126' mg/dL signals diabetes
7. restecg - resting electrocardiographic results 0: Nothing to note 1: ST-T Wave abnormality can range from mild symptoms to severe problems signals non-normal heart beat 2: Possible or definite left ventricular hypertrophy Enlarged heart's main pumping chamber
8. thalach - maximum heart rate achieved
9. exang - exercise induced angina (1 = yes; 0 = no)
10. oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more
11. slope - the slope of the peak exercise ST segment 0: Upsloping: better heart rate with excercise (uncommon) 1: Flatsloping: minimal change (typical healthy heart) 2: Downslopins: signs of unhealthy heart
12. ca - number of major vessels (0-3) colored by flourosopy colored vessel means the doctor can see the blood passing through the more blood movement the better (no clots)
13. thal - thalium stress result 1,3: normal 6: fixed defect: used to be defect but ok now 7: reversable defect: no proper blood movement when excercising
14. target - have disease or not (1=yes, 0=no) (= the predicted attribute)

• The dataset have total of 303 rows × 14 columns.

• Getting the target value_counts

• Checking the Heart Disease frequency with respect to Sex.

• More number of males have Heart Disease compare to females.

• Heart Disease frequency with age, with respect to max heart rate .

• Age count

• Heart Disease with respect to Chest Pain Type

Correlation Matrix

Machine Learning

• Splitting the data into train and test model. Have to find how many have heart disease or not assigning target column to 'y' and other columns to 'X'.

• Using following machine learning models from scikit-learn.

• RandomForestClassifier()

• KNearestNeighbor()

• LogisticRegression()

• For each models we get different scores as follows:

• {'Logistic Regression': 0.8852459016393442, 'KNN': 0.6885245901639344, 'Random Forest': 0.8360655737704918}

• Model Comparison

• Correlation Matrix

• From the model it is clear that KNN performs very poor compared to other two models.
• We will do the hyperparameter tuning using RandomizedSearchCV and GridSearchCV on the other two models.

RandomizedSearchCV

• Hyperparameter tuning of LogisticRegression

• Following parameters were used {"C": np.logspace(-4, 4, 20), "solver": ["liblinear"]}

• The best parameters are {'solver': 'liblinear', 'C': 0.23357214690901212}

• With RandomizedSearchCV tuning the score is 0.885

• Hyperparameter tuning of RandomForestClassifier

• Following parameters were used {"n_estimators": np.arange(10, 1000, 50), "max_depth": [None, 3, 5, 10], "min_samples_split": np.arange(2, 20, 2), "min_samples_leaf": np.arange(1, 20, 2)}

• The best parameters are {'n_estimators': 210, 'min_samples_split': 4, 'min_samples_leaf': 19, 'max_depth': 3}

• With RandomizedSearchCV tuning the score is 0.885

GridSearchCV

• Since LogisticRegression score was better than RandomForestClassifier will do tuning of LogisticRegression again.
• Hyperparameter tuning of LogisticRegression
• Following parameters were used {"C": np.logspace(-4, 4, 30), "solver": ["liblinear"]}
• The best parameters are {'C': 0.20433597178569418, 'solver': 'liblinear'}

Evaluation

• Evaluation of the model is done using classification_report and confusion_matrix.

Conclusion

• From Hyperparameter tuning of models we can conclude that LogisticRegression has more accuracy score, which will help in predicting the Heart Disease among the people.