End-to-end-Heart-Disease-Classification

Predicting heart disease using machine learing. this Project 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.

We're going to take the following approach: 1.Problem definition 2.Data 3.Evaluation 4.Features 5.Modelling 6.Experimentation

1. Problem Definition

In a statement ,

Given clinical parameters about a patient, can we predict whether or not they have heart disease?

2. Data

The original data came from the cleavland data from the UCI Machine Learning Repoitory.https://archive.ics.uci.edu/dataset/45/heart+disease

There is also a version of it available on Kaggle. https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset

3. Evaluation

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

4. Features

This is where you'll get different information about each of the features in your data.

Create data dictionary

• 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
• arget - have disease or not (1=yes, 0=no) (= the predicted attribute)