Given clinical parameters about a patient, can we predict whether or not they have heart disease? Yes we can ! This model uses a set of clinical data about a patient to predict whether or not they have cardiac disease.
- Main Code : https://github.com/piyush033/Heart-Disease-Classification-Model/blob/main/Heart_Disease_Project.ipynb
The original data came from the Cleveland data from UCI Machine Learning 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
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Scikit-Learn
A data dictionary describes the data you're dealing with. Not all datasets come with them so this is where you may have to do your research or ask a subject matter expert (someone who knows about the data) for more. 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 cholesterol 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 exercise unhealthy heart will stress more
- slope - the slope of the peak exercise ST segment 0: Upsloping: better heart rate with exercise (uncommon) 1: Flat Sloping: minimal change (typical healthy heart) 2: Downsloping: signs of unhealthy heart
- ca - number of major vessels (0-3) colored by fluoroscopy colored vessel means the doctor can see the blood passing through the more blood movement the better (no clots)
- thal - thallium stress result 1,3: normal 6: fixed defect: used to be defect but ok now 7: reversible defect: no proper blood movement when exercising
- target - have disease or not (1=yes, 0=no) (= the predicted attribute)
Data :
