A Hybrid Classification System for Heart Disease Diagnosis Based on the RFRS Method

Heart disease is one of the most common diseases in the world. The objective of this study is to aid the diagnosis of heart disease using a hybrid classification system based on the ReliefF and Rough Set (RFRS) method. The proposed system contains two subsystems: the RFRS feature selection system and a classification system with an ensemble classifier. The first system includes three stages: (i) data discretization, (ii) feature extraction using the ReliefF algorithm, and (iii) feature reduction using the heuristic Rough Set reduction algorithm that we developed. In the second system, an ensemble classifier is proposed based on the C4.5 classifier. The Statlog (Heart) dataset, obtained from the UCI database, was used for experiments. A maximum classification accuracy of 92.59% was achieved according to a jackknife cross-validation scheme. The results demonstrate that the performance of the proposed system is superior to the performances of previously reported classification techniques

A Review of Feature Selection and Classification Approaches for Heart Disease Prediction

Cardiovascular disease has been the number one illness to cause death in the world for years. As information technology develops, many researchers have conducted studies on a computer-assisted diagnosis for heart disease. Predicting heart disease using a computer-assisted system can reduce time and costs. Feature selection can be used to choose the most relevant variables for heart disease. It includes filter, wrapper, embedded, and hybrid. The filter method excels in computation speed. The wrapper and embedded methods consider feature dependencies and interact with classifiers. The hybrid method takes advantage of several methods. Classification is a data mining technique to predict heart disease. It includes traditional machine learning, ensemble learning, hybrid, and deep learning. Traditional machine learning uses a specific algorithm. The ensemble learning combines the predictions of multiple classifiers to improve the performance of a single classifier. The hybrid approach combines some techniques and takes advantage of each method. Deep learning does not require a predetermined feature engineering. This research provides an overview of feature selection and classification methods for the prediction of heart disease in the last ten years. Thus, it can be used as a reference in choosing a method for heart disease prediction for future research

Cuckoo inspired algorithms for feature selection in heart disease prediction

Heart disease is a predominant killer disease in various nations around the globe. However, this is because the default medical diagnostic techniques are not affordable by common people. This inspires many researchers to rescue the situation by using soft computing and machine learning approaches to bring a halt to the situation. These approaches use the medical data of the patients to predict the presence of the disease or not. Although, most of these data contains some redundant and irrelevant features that need to be discarded to enhance the prediction accuracy. As such, feature selection has become necessary to enhance prediction accuracy and reduce the number of features. In this study, two different but related cuckoo inspired algorithms, cuckoo search algorithm (CSA) and cuckoo optimization algorithm (COA), are proposed for feature selection on some heart disease datasets. Both the algorithms used the general filter method during subset generation. The obtained results showed that CSA performed better than COA both concerning fewer number of features as well as prediction accuracy on all the datasets. Finally, comparison with the state of the art approaches revealed that CSA also performed better on all the datasets

The Analysis of Performace Model Tiered Artificial Neural Network for Assessment of Coronary Heart Disease

The assessment model of coronary heart disease is so much developed in line with the development of information technology, particularly the field of artificial intelligence. Unfortunately, the assessment models developed mostly do not use such an approach made by the clinician, the tiered approach. This study aims to analyze the performance of a tiered model assessment. The method used for each level is, preprocessing, building architecture artificial neural network (ANN), conduct training using the Levenberg-Marquardt algorithm and one step secant, as well as testing the system. The study is divided into the terms of the stages in the examination procedure. The test results showed the influence of each level, both when the output level of the previous positive or negative, were tested back at the next level. The performance evaluation may indicate that the top level provides performance improvement and or reinforce the previous level.

A K -means Interval Type-2 Fuzzy Neural Network for Medical Diagnosis

Abstract(#br)This paper proposes a new medical diagnosis algorithm that uses a K -means interval type-2 fuzzy neural network (KIT2FNN). This KIT2FNN classifier uses a K -means clustering algorithm as the pre-classifier and an interval type-2 fuzzy neural network as the main classifier. Initially, the training data are classified into k groups using the K -means clustering algorithm and these data groups are then used sequentially to train the structure of the k classifiers for the interval type-2 fuzzy neural network (IT2FNN). The test data are also initially used to determine to which classifier they are best suited and then they are inputted into the corresponding main classifier for classification. The parameters for the proposed IT2FNN are updated using the steepest descent gradient..

Feature elimination and stacking framework for accurate heart disease detection in IoT healthcare systems using clinical data

IntroductionHeart disease remains a complex and critical health issue, necessitating accurate and timely detection methods.MethodsIn this research, we present an advanced machine learning system designed for efficient and precise diagnosis of cardiac disease. Our approach integrates the power of Random Forest and Ada Boost classifiers, along with incorporating data pre-processing techniques such as standard scaling and Recursive Feature Elimination (RFE) for feature selection. By leveraging the ensemble learning technique of stacking, we enhance the model's predictive performance by combining the strengths of multiple classifiers.ResultsThe evaluation metrics results demonstrate the superior accuracy and obtained the higher performance in terms of accuracy, 99.25%. The effectiveness of our proposed system compared to baseline models.DiscussionFurthermore, the utilization of this system within IoT-enabled healthcare systems shows promising potential for improving heart disease diagnosis and ultimately enhancing patient outcomes

Minimum Relevant Features to Obtain Explainable Systems for Predicting Cardiovascular Disease Using the Statlog Data Set

Learning systems have been focused on creating models capable of obtaining the best results in error metrics. Recently, the focus has shifted to improvement in the interpretation and explanation of the results. The need for interpretation is greater when these models are used to support decision making. In some areas, this becomes an indispensable requirement, such as in medicine. The goal of this study was to define a simple process to construct a system that could be easily interpreted based on two principles: (1) reduction of attributes without degrading the performance of the prediction systems and (2) selecting a technique to interpret the final prediction system. To describe this process, we selected a problem, predicting cardiovascular disease, by analyzing the well-known Statlog (Heart) data set from the University of California’s Automated Learning Repository. We analyzed the cost of making predictions easier to interpret by reducing the number of features that explain the classification of health status versus the cost in accuracy. We performed an analysis on a large set of classification techniques and performance metrics, demonstrating that it is possible to construct explainable and reliable models that provide high quality predictive performance

Health Care Automation in Compliance to Industry 4.0 Standards: A Case Study of Liver Disease Prediction

The industrial internet contributes to the standards of Industry 4.0, which involve handling large volumes of data using advanced soft-computing techniques. Machine Learning (ML) is an advanced soft-computing technique that plays a critical role in predicting and detecting serial chronic diseases, thereby automating the diagnosis. The process constitutes and uses several data mining algorithms and methods for efficient medical data analysis. Recent studies on several chronic diseases, liver disorders and diseases associated with the organ have been fatal. In this paper, the liver patient dataset from India is considered and investigated for developing a classification model. Liver disease is a dangerous, life-threatening disease often diagnosed false positive. Mild liver enlargement, improper or ambiguous functionality over a brief period, is prominent even in healthy people, which has become the main reason for ignoring the same at the early stage. It is essential to predict liver disease through the parameters and their values from the liver functionality test sensing the behavior of similar patients who were ignored in the initial stage. In this paper, the machine learning technique is demonstrated to predict liver disease using the liver function test data of the 580 patients as training data. The model has been developed with an accuracy of approximately 75%. The simulation-based experiment is based on the publicly available dataset and can be extended to any native set to predict the patients' health quickly. The Random Forest Algorithm is used to develop the model in Matlab, and the analysis is carried out using parameters like total bilirubin, alkaline phosphotase, alamine aminotransferase, total proteins, and A/G ratio