A Model Built Using Kaggle Dataset & Machine Learning Classification Algorithms such as Logistics Regression,K-NN, Naive Bayes, SVM, Decision Tree & Random forest which Predicts chances of heart disease in a person.
logistic regression=86.89%
K-NN=88.52%
SVM=86.52%
Navies Bayes=86.89%
Decision tree=78.69%
Random Forest=88.52%
https://www.kaggle.com/ronitf/heart-disease-uci
age - age in years
sex - (1 = male; 0 = female)
cp - chest pain type
trestbps - resting blood pressure (in mm Hg on admission to the hospital)
chol - serum cholestoral in mg/dl
fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)
restecg - resting electrocardiographic results
thalach - maximum heart rate achieved
exang - exercise induced angina (1 = yes; 0 = no)
oldpeak - ST depression induced by exercise relative to rest
slope - the slope of the peak exercise ST segment
ca - number of major vessels (0-3) colored by flourosopy
thal - 3 = normal; 6 = fixed defect; 7 = reversable defect
target - have disease or not (1=yes, 0=no)