Heart failure prediction based on random forest algorithm using genetic algorithm for feature selectio

Abstract

A disorder or illness called heart failure results in the heart becoming weak or damaged. In order to avoid heart failure early on, it is crucial to understand the causes of heart failure. Based on validation, two experimental processing steps will be applied to the dataset of clinical records related to heart failure. Testing will be done in the first step utilizing six different classification algorithms, including K-nearest neighbor, neural network, random forest, decision tree, Naïve Bayes, and support vector machine (SVM). Cross-validation was employed to conduct the test. According to the results, the random forest algorithm performed better than the other five algorithms in tests employing the algorithm. Subsequent testing uses an algorithm with the best accuracy value, which will then be tested again using split validation with varying split ratios and genetic algorithms as a selection feature. The value generated from testing using the genetic algorithm selection feature is better than the random forest algorithm alone, which is recorded to produce an accuracy value of 93.36% in predicting the survival of heart failure patients.

Accuracy enhancement with artificial neural networks for bipolar disorder prediction

“…The process of multiplying weights with each feature and adding them with bias is called forward propagation whereas the process of updating the weights in the model is backward propagation which requires optimization and loss function [27]. The output of neuron is the sum of all the values of neuron in the previously connected layer [29].…”

“…They proposed genetic algorithm to predict accuracy better as compared to other algorithms for the survival of heart attack failure patients [26]. They combined CNN technique to enhance feature extraction and classification accuracy and a classification with CNN to identify dysgraphia in children having accuracy of 91% [27], [28]. The proposed Alzheimer's disease-3 deep CNN (AD-3DCNN) model has highest accuracy for predicting different stages of AD as compared to other existing models [29].…”