Machine Learning Models for Heart Disease Prediction

PCA: PCA is a dimensionality-reduction algorithm that is used to reduce noise and boost accuracy in machine learning.

Principle components are smaller subset of variables made from potentially correlated variables

in the original data. PCA is an important tool for simplifying highly dimensional

data while retaining critical information and variance Models Used: Now we arrive at the

core of our methodology: the machine learning models we used. We selected three distinct classification

algorithms to assess their predictive power on our heart disease dataset. First,

Logistic Regression. This is a supervised learning algorithm and a type of generalized linear model.

It is a good starting point because it is known for being simple, fast,