Thus preventing Heart diseases has become more than necessary. Good data-driven systems for predicting heart diseases can improve the entire research and prevention process, making sure that more people can live healthy lives. This is where Machine Learning comes into play. Machine Learning helps in predicting the Heart diseases, and the predictions made are quite accurate.
The project involved analysis of the heart disease patient dataset with proper data processing. Then, different models were trained and and predictions are made with different algorithms KNN, Decision Tree, Random Forest,SVM,Logistic Regression etc This is the jupyter notebook code and dataset I've used for my Kaggle kernel 'Binary Classification with Sklearn and Keras'
I've used a variety of Machine Learning algorithms, implemented in Python, to predict the presence of heart disease in a patient. This is a classification problem, with input features as a variety of parameters, and the target variable as a binary variable, predicting whether heart disease is present or not.
A huge number of methodologies of available algorithms are not appropriate to aid professionals under the different area, highly depends on the employed statistics, therefore, struggle to maintain recitation with the variation of data properties.
The majority of the algorithms intended to accomplish only a specific task hence does not fit for the real-time scenario where the cost is one of the primary factors
The exploitation of the random method is one of the key issues and the implementation of the method is based on the modified approach without consideration.
The formation of considerable false prediction is still a challenging issue of accessible algorithms.
Similar to the classical approaches most of the recently suggested algorithms have unique restraints and possess the urge of human efforts to destroy the obstacles.
HERE
Machine Learning algorithms used:
Logistic Regression (Scikit-learn)
Support Vector Machine (Linear) (Scikit-learn)
K-Nearest Neighbours (Scikit-learn)
Decision Tree (Scikit-learn)
Random Forest (Scikit-learn)
XGBoost (Scikit-learn)
Accuracy achieved: 95% (Random Forest)