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Heart Disease Prediction

In this model we have predicted the risk of heart disease based on real dataset that we got from a medical university chennai.

DATASET

The dataset we used contians more than 150 patient records and their daily routine data. As a result it helps our model to train with realtime data.Our data set contains many features like age,Dietary hahbit,Alcohol consumer or not etc...

Dataset : https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Data Preprocessing:

We have preprocessed our dataset using Sk-learn. sk-learn:

The sklearn.preprocessing package provides several common utility functions and transformer classes to change raw feature vectors into a representation that is more suitable for the downstream estimators. In general, learning algorithms benefit from standardization of the data set.

Models Used:

  1. SVM
  2. Naive Bayes
  3. Logistic Regression
  4. Decision Tree
  5. Random Forest
  6. LightGBM
  7. XGboost

Model's Result Prediction Percentage:

SVM: Support Vector Machine

    ~Training Set Prediction : 0.6694214876033058
    
    ~Testing Set Prediction : 0.5737704918032787

Naive Bayes: Works based on Naive Bayes algorithm

    ~Training Set Prediction : 0.8677685950413223
    
    ~Testing Set Prediction :  0.7868852459016393

Logistic Regression: Logistic regression estimates the probability of an disease based on a given dataset of independent variables

    ~Training Set Prediction : 0.8636363636363636
    
    ~Testing Set Prediction : 0.8032786885245902

Decision Tree: A decision tree is a non-parametric supervised learning algorithm, which is utilized for both classification and regression tasks.

    ~Training Set Prediction : 1.0     
    
    ~Testing Set Prediction :  0.7704918032786885

Random Forest: Works based on Naive Bayes algorithm

    ~Training Set Prediction : 1.0
    
    ~Testing Set Prediction : 0.7704918032786885

LightGBM: Gradient Boosting Decision Tree (GBDT) algorithm with the addition of two novel techniques: Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB).

    ~Training Set Prediction : 0.9958677685950413
    
    ~Testing Set Prediction :0.7704918032786885

XGBoost: It builds a decision tree for a given boosting iteration, one level at a time, processing the entire dataset concurrently on the GPU.

    ~Training Set Prediction : 0.987603305785124
    
    ~Testing Set Prediction : 0.7540983606557377

CONCLUDED MODEL FROM PREDICTION From the prediction results Logistic Regression is more suitable for both training and testing data .