Enhancing the Precision of Heart Disease Risk Prediction Using Mining & Classification Techniques

Machine learning, is used to address a variety of data science issues. Predicting a result based on data that already exists is a popular use of machine learning. In order to anticipate the result, the machine applies patterns it has learned from the existing dataset to an unknown dataset. One effective machine learning method that is frequently used for prediction is classification. While some categorization systems show limited accuracy, others make predictions with satisfactory accuracy.
This work examines a technique called ensemble classification, which combines different classifiers, such as Random Forest Classifier and MLP Classifier to increase the accuracy of weak methods. A dataset on cardiac diseases was used to do experiments with this tool. In “addition to improving the accuracy of weak classification algorithms, this research demonstrates the algorithm's usefulness in disease prediction at an early stage by implementing it on a medical dataset.Results from this research show that ensemble methods, such as the bagging technique, perform satisfactorily in determining heart disease risk and are useful in increasing the prediction accuracy of weak classifiers.
A feature selection implementation significantly improved the process's performance, and the outcomes demonstrated a notable increase in prediction accuracy. Bagging is one of the ensemble strategies that improve prediction accuracy for Deep Neural Network (DNN) and long short-term memory” (LSTM), respectively. Ensemble classification helped weak classifiers achieve an accuracy gain of up to 10%.

Cardiac disease, Machine learning, Ensemble classifier, Random Forest Classifier, MLP Classifier.

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