Heart Disease Classification problem

This notebook looks into Heart Disease dataset using various Python libraries to build a ML model for prediction whether or not someone has heart disease based on their medical attributes.

Plan:

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

1. Problem definition:

binary classification problem, 2 classes, has heart disase, doesn't have heart disease Given clinical parameters about a patient, can we predict whether or not they have heart disease?

2. Data:

The data came from the Clevland data from the UCI ML Repository. https://archive.ics.uci.edu/ml/datasets/heart+disease

There is also a version of it available on Kaggle. https://www.kaggle.com/datasets/redwankarimsony/heart-disease-data

3. Problem Evaluation

If we can reach 95% accuracy at predicting whether or not a patient has heart disease during the proof of concept, we'll pursue the project.

4. Features

Dataset dictionary:

The following are the features we'll use to predict our target variable (heart disease or no heart disease).

• age - age in years
• sex - (1 = male; 0 = female)
• 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
• trestbps - resting blood pressure (in mm Hg on admission to the hospital) anything above 130-140 is typically cause for concern
• chol - serum cholestoral in mg/dl serum = LDL + HDL + .2 * triglycerides above 200 is cause for concern
• fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) '>126' mg/dL signals diabetes
• 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
• thalach - maximum heart rate achieved
• exang - exercise induced angina (1 = yes; 0 = no)
• oldpeak - ST depression induced by exercise relative to rest looks at stress of heart during excercise unhealthy heart will stress more
• 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
• 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)
• 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
• target - have disease or not (1=yes, 0=no) (= the predicted attribute)