scholarly journals Hybrid System of Tiered Multivariate Analysis and Artificial Neural Network for Coronary Heart Disease Diagnosis

2017 ◽  
Vol 7 (2) ◽  
pp. 1023 ◽  
Author(s):  
Wiharto Wiharto ◽  
Hari Kusnanto ◽  
Herianto Herianto
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Feature Selection ◽  
Multivariate Analysis ◽  
Heart Disease ◽  
System Performance ◽  
Selection Process ◽  
Disease Diagnosis ◽  
Features Selection ◽  
Diagnosis System

<span lang="EN-US">Improved system performance diagnosis of coronary heart disease becomes an important topic in research for several decades. One improvement would be done by features selection, so only the attributes that influence is used in the diagnosis system using data mining algorithms. Unfortunately, the most feature selection is done with the assumption has provided all the necessary attributes, regardless of the stage of obtaining the attribute, and cost required. This research proposes a hybrid model system</span><span> for</span><span lang="EN-US"> diagnosis of coronary heart disease. System diagnosis preceded the feature selection process, using tiered multivariate analysis. The analytical method used is logistic regression. The next stage, the classification by using multi-layer perceptron neural network. Based on test results, system performance proposed value</span><span> for</span><span lang="EN-US"> accuracy 86.3%, sensitivity 84.80%, specificity 88.20%, positive prediction value (PPV) 90.03%, negative prediction value (NPV) 81.80%</span><span>, accuracy 86,30% </span><span lang="EN-US"> and area under the curve (AUC) of 92.1%. The performance of a diagnosis using a combination attributes of risk factors,</span><span lang="EN-US">symptoms and exercise ECG. The conclusion that can be drawn</span><span> is</span><span lang="EN-US"> that the proposed diagnosis system capable of delivering performance in the </span><span>very good</span><span lang="EN-US"> category, with a number of attributes that are not a lot of checks and a relatively low cost</span><span>.</span>

scholarly journals K-Fold Cross Validation for Selection of Cardiovascular Disease Diagnosis Features by Applying Rule-Based Datamining

2019 ◽  
Vol 1 (2) ◽  
pp. 23-35
Author(s):  
Dwi Normawati ◽  
Dewi Pramudi Ismi
Keyword(s):  
Coronary Heart Disease ◽  
Feature Selection ◽  
Heart Disease ◽  
Rough Set ◽  
Cross Validation ◽  
Rough Set Theory ◽  
Disease Diagnosis ◽  
Classification Performance ◽  
Rule Based ◽  
Fold Cross Validation

Coronary heart disease is a disease that often causes human death, occurs when there is atherosclerosis blocking blood flow to the heart muscle in the coronary arteries. The doctor's referral method for diagnosing coronary heart disease is coronary angiography, but it is invasive, high risk and expensive. The purpose of this study is to analyze the effect of implementing the k-Fold Cross Validation (CV) dataset on the rule-based feature selection to diagnose coronary heart disease, using the Cleveland heart disease dataset. The research conducted a feature selection using a medical expert-based (MFS) and computer-based method, namely the Variable Precision Rough Set (VPRS), which is the development of the Rough Set theory. Evaluation of classification performance using the k-Fold method of 10-Fold, 5-Fold and 3-Fold. The results of the study are the number of attributes of the feature selection results are different in each Fold, both for the VPRS and MFS methods, for accuracy values obtained from the average accuracy resulting from 10-Fold, 5-Fold and 3-Fold. The result was the highest accuracy value in the VPRS method 76.34% with k = 5, while the MTF accuracy was 71.281% with k = 3. So, the k-fold implementation for this case is less effective, because the division of data is still structured, according to the order of records that apply in each fold, while the amount of testing data is too small and too structured. This affects the results of the accuracy because the testing rules are not thoroughly represented

scholarly journals Taylor Bird Swarm Algorithm Based on Deep Belief Network for Heart Disease Diagnosis

2020 ◽  
Vol 10 (18) ◽  
pp. 6626 ◽  
Author(s):  
Afnan M. Alhassan ◽  
Wan Mohd Nazmee Wan Zainon
Keyword(s):  
Feature Selection ◽  
Heart Disease ◽  
Selection Process ◽  
Disease Diagnosis ◽  
Medical Data ◽  
Deep Belief Network ◽  
Belief Network ◽  
Swarm Algorithm ◽  
Bird Swarm Algorithm ◽  
Heart Disease Diagnosis

Contemporary medicine depends on a huge amount of information contained in medical databases. Thus, the extraction of valuable knowledge, and making scientific decisions for the treatment of disease, has progressively become necessary to attain effective diagnosis. The obtainability of a large amount of medical data leads to the requirement of effective data analysis tools for extracting constructive knowledge. This paper proposes a novel method for heart disease diagnosis. Here, the pre-processing of medical data is done using log-transformation that converts the data to its uniform value range. Then, the feature selection process is performed using sparse fuzzy-c-means (FCM) for selecting significant features to classify medical data. Incorporating sparse FCM for the feature selection process provides more benefits for interpreting the models, as this sparse technique provides important features for detection, and can be utilized for handling high dimensional data. Then, the selected features are given to the deep belief network (DBN), which is trained using the proposed Taylor-based bird swarm algorithm (Taylor-BSA) for detection. Here, the proposed Taylor-BSA is designed by combining the Taylor series and bird swarm algorithm (BSA). The proposed Taylor-BSA–DBN outperformed other methods, with maximal accuracy of 93.4%, maximal sensitivity of 95%, and maximal specificity of 90.3%, respectively.

scholarly journals Neural Network Model for Heart Disease Diagnosis

2020 ◽  
Vol 7 ◽  
Keyword(s):  
Neural Network ◽  
Heart Disease ◽  
Network Model ◽  
Loss Function ◽  
Neural Network Model ◽  
Disease Diagnosis ◽  
Batch Size ◽  
Diagnosis System ◽  
Simulation Results ◽  
Heart Disease Diagnosis

The heart disease diagnosis system is proposed inthis study. This kind of diagnosis systems enhance medical careand helps doctors. In this paper, heart disease dataset fromkaggle web site is used. Neural Network is examined andanalyzed for different structures as an optimizer, loss function,and batch size. The simulation results show that the proposedneural network model has 90,16% accuracy.

scholarly journals An Improved Coronary Heart Disease Predictive System Using Random Forest

2021 ◽  
pp. 17-27
Author(s):  
Abdulraheem Abdul ◽  
Rafiu M. Isiaka ◽  
Ronke S. Babatunde ◽  
Jumoke F. Ajao
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Feature Selection ◽  
Heart Disease ◽  
Random Forest ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Support Vector ◽  
Disease Prediction ◽  
K Nearest Neighbor

Aims: This work aim is to develop an enhanced predictive system for Coronary Heart Disease (CHD). Study Design: Synthetic Minority Oversampling Technique and Random Forest. Methodology: The Framingham heart disease dataset was used, which was collected from a study in Framingham, Massachusetts, the data was cleaned, normalized, rebalanced. Classifiers such as random forest, artificial neural network, naïve bayes, logistic regression, k-nearest neighbor and support vector machine were used for classification. Results: Random Forest outperformed other classifiers with an accuracy of 98%, a sensitivity of 99% and a precision of 95.8%. Feature selection was employed for better classification, but  no significant improvement was recorded on the performance of the classifier with feature selection. Train test split also performed better that cross validation. Conclusion: Random Forest is recommended for research in Coronary Heart Disease prediction domain.

scholarly journals Heart Risk Failure Prediction Using a Novel Feature Selection Method for Feature Refinement and Neural Network for Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Ashir Javeed ◽  
Sanam Shahla Rizvi ◽  
Shijie Zhou ◽  
Rabia Riaz ◽  
Shafqat Ullah Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Heart Disease ◽  
Prediction Accuracy ◽  
Feature Selection Method ◽  
Disease Diagnosis ◽  
Selection Method ◽  
Diagnostic Systems ◽  
Operating Characteristics ◽  
Heart Disease Diagnosis

Diagnosis of heart disease is a difficult job, and researchers have designed various intelligent diagnostic systems for improved heart disease diagnosis. However, low heart disease prediction accuracy is still a problem in these systems. For better heart risk prediction accuracy, we propose a feature selection method that uses a floating window with adaptive size for feature elimination (FWAFE). After the feature elimination, two kinds of classification frameworks are utilized, i.e., artificial neural network (ANN) and deep neural network (DNN). Thus, two types of hybrid diagnostic systems are proposed in this paper, i.e., FWAFE-ANN and FWAFE-DNN. Experiments are performed to assess the effectiveness of the proposed methods on a dataset collected from Cleveland online heart disease database. The strength of the proposed methods is appraised against accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and receiver operating characteristics (ROC) curve. Experimental outcomes confirm that the proposed models outperformed eighteen other proposed methods in the past, which attained accuracies in the range of 50.00–91.83%. Moreover, the performance of the proposed models is impressive as compared with that of the other state-of-the-art machine learning techniques for heart disease diagnosis. Furthermore, the proposed systems can help the physicians to make accurate decisions while diagnosing heart disease.

CURRENT ASPECTS OF CORONARY HEART DISEASE DIAGNOSIS AND TREATMENT

2020 ◽  
pp. 5-10
Author(s):  
O. M. Korzh
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Drug Therapy ◽  
Coronary Arteries ◽  
Artery Bypass ◽  
Myocardial Revascularization ◽  
Disease Diagnosis ◽  
Percutaneous Coronary Angioplasty ◽  
Mortality And Morbidity ◽  
Intravascular Thrombosis

Among the cardiovascular diseases associated with atherosclerosis, chronic coronary heart disease, including angina, is the most common form. It is the myocardium lesion that develops as a result of an imbalance between the coronary circulation and metabolic needs of heart muscle. The presence of angina symptoms often indicates a pronounced narrowing of one or more coronary arteries, but also occurs in non−obstructive arterial impairment and even in normal coronary arteries. Factors of functional damage to the coronary arteries are spasm, temporary platelet aggregation and intravascular thrombosis. Today there are opportunities not only to use the therapy with proven effectiveness, aimed at reducing the risk of complications, including fatal, but also to treat angina (ischemia), which improves the patient's life quality. The drug protocol includes the ones with a proven positive effect on this disease prognosis, which are mandatory if there are no direct contraindications to use, as well as a large group of antianginal or anti−ischemic drugs. The choice of a particular drug or its combinations with other drugs is carried out in accordance with generally accepted recommendations: taking into account the individual approach, the severity of angina, hemodynamic parameters (heart rate and blood pressure, presence of comorbid conditions). If drug therapy is ineffective, the option of coronary myocardial revascularization (percutaneous coronary angioplasty or coronary artery bypass grafting) is considered. Due to the high mortality and morbidity rates of coronary heart disease worldwide, one of the priorities of practical health care is the prevention of diseases caused by atherosclerosis. Key words: coronary heart disease, angina, family physician, prognosis, drug therapy.

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