Heart Disease Prediction under Machine Learning and Association Rules under Neutrosophic Environment
Journal Title: Neutrosophic Systems with Applications - Year 2023, Vol 10, Issue 1
Abstract
Early identification and precise prediction of heart disease have important implications for preventative measures and better patient outcomes since cardiovascular disease is a leading cause of death globally. By analyzing massive amounts of data and seeing patterns that might aid in risk stratification and individualized treatment planning, machine learning algorithms have emerged as valuable tools for heart disease prediction. Predictive modeling is considered for many forms of heart illness, such as coronary artery disease, myocardial infarction, heart failure, arrhythmias, and valvar heart disease. Resource allocation, preventative care planning, workflow optimization, patient involvement, quality improvement, risk-based contracting, and research progress are all discussed as management implications of heart disease prediction. The effective application of machine learning-based cardiac disease prediction models requires collaboration between healthcare organizations, providers, and data scientists. This paper used three tools such as the neutrosophic analytical hierarchy process (AHP) as a feature selection, association rules, and machine learning models to predict heart disease. The neutrosophic AHP method is used to compute the weights of features and select the highest features. The association rules are used to give rules between values in all datasets. Then, we used the neutrosophic AHP as feature selection to select the best feature to input in machine learning models. We used nine machine learning models to predict heart disease. We obtained the random forest (RF) and decision tree (DT) have the highest accuracy with 100%, followed by Bagging, k-nearest neighbors (KNN), and gradient boosting have 99%, 98%, and 97%, then AdaBoosting has 89%, then logistic regression and Naïve Bayes have 84%, then the least accuracy is support vector machine (SVM) has 68%.
Authors and Affiliations
Ahmed A. El-Douh, SongFeng Lu, Ahmed Abdelhafeez, Ahmed M. Ali, Alber S. Aziz
Keywords
Related Articles
- EP ID EP729583
- DOI https://doi.org/10.61356/j.nswa.2023.75
- Views 56
- Downloads 0
Waste Reduction and Recycling: Schweizer-Sklar Aggregation Operators Based on Neutrosophic Fuzzy Rough Sets and Their Application in Green Supply Chain Management
Green supply chain management (GSCM) is a valuable application that is used to reduce the overall environmental impact of the supply chain. Waste reduction and recycling are crucial components of sustainable technique th...
Neutrosophic Bicubic B-spline Surface Interpolation Model for Uncertainty Data
Dealing with the uncertainty data problem using neutrosophic data is difficult since certain data are wasted due to noise. To address this issue, this work proposes a neutrosophic set (NS) strategy for interpolating the...
An Introduction to Bipolar Pythagorean Refined Sets
The aim of this paper is to introduce the new concept of a Bipolar Pythagorean refined set by combining the two notions called a Bipolar Pythagorean set and a Pythagorean refined set. Also, basic operations and algebraic...
Software Reliability Model Estimation for an Indeterministic Crime Cluster through Reinforcement Learning
The software reliability model estimates the probability of data failure in a specific environment, significantly impacting reliability and trustworthiness. The paper study focuses on cluster crime data, i.e., indetermin...
NCMPy: A Modelling Software for Neutrosophic Cognitive Maps based on Python Package
Cognitive maps are a vital tool that can be used for knowledge representation and reasoning. Fuzzy Cognitive Maps (FCMs) are popular soft computing techniques used to model large and complex systems, and they can aid in...