heart-disease-detection-analysis

Predicting heart-disease using machine-learning

predicting whether someone has heart-disease or not based on data science and machine-learning tools.

We're going to take the following approach:

1. problem definition
2. data
3. evaluation.
4. features
5. modelling
6. experimentation

1. Problem Definition

Given clinical parameters,

we have to predict whether some patient has heart-disease or not.

2. Data

The original data came from the Cleavland data from the 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/ronitf/heart-disease-uci

3. 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

Details about different columns in heart-disease file.

Create data dictionary

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)