Abstract:
Heart failure (HF) is one of the most common causes of death and morbidity in the world
and poses to be a serious problem in early diagnosis and survival prognosis. In this study
for predicting heart disease survival using a dataset of 5000 patients. Precise and early
prognostication potential in automating and improving survival analysis. This paper
documents the comparison of different ML techniques applicable to predict survival
among HF patients: Random Forest, Decision Tree, Gradient Boosting, K-Nearest
Neighbours, Support Vector Machine, Ad Boost, Logistic Regression, and Naive Bayes.
This study will be based on the data that we will use to include some of the clinical
parameters that were read by the patients who had heart failure. Before training the
models, data pre-processing, balancing with ADASYN and feature scaling have been
used. The assessment was done based on standard metrics of performance, including
accuracy, precision, recall, F1-score, and ROC AUC. Model performance was analysed
using visualization tools such as a confusion matrix, ROC, and importance of features
plots. In this study, using analogical algorithms depends on accuracy, precision, recall,
F1-score, and Random Forest (RF) shows the highest accuracy of of survival events
among patients with HF also continues to be an imminent obstacle because of the
heterogeneous and complex characteristics of the disease. Nonetheless, the current
developments in machine learning (ML) have demonstrated 99.5%.
