Applying Feature Selection and Weight Optimization Techniques to Enhance Artificial Neural Network for Heart Disease Diagnosis

The number of factors that can be categorized into the diagnosis of Chronic Kidney Disease (CKD) at an early stage makes information about the diagnosis of the disease divided into information that has many influences and has little influence. This study aims to select diagnoses in medical records with the most influential information on chronic kidney disease. The first step is to select a diagnosis with much influence by implementing the Sequential Backward Feature Selection (SBFS). This algorithm eliminates features that are considered to have little influence when compared to other features. In the second step, the features of the best diagnoses are used as input to the Artificial Neural Network (ANN) classification algorithm. The results obtained from this study are information in the form of the best diagnoses that have much influence on chronic kidney disease and the accuracy results based on the selected diagnoses. Based on the study results, 15 features are considered the best of the 18 features used to achieve 88% accuracy results. Compared with conventional methods, this method still requires consideration from the medical staff because it is not a final diagnosis for patients.

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Thrice Filtered Information Energy Based Particle Swarm Feature Selection (TFIE-PSFS) Method Based Artificial Neural Network Classification for Improving Heart Disease Diagnosis

Asian Journal of Engineering and Applied Technology ◽

10.51983/ajeat-2019.8.1.1068 ◽

2019 ◽

Vol 8 (1) ◽

pp. 1-8

Author(s):

P. Umasankar ◽

V. Thiagarasu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Heart Disease ◽

Back Propagation ◽

Network Models ◽

Disease Diagnosis ◽

Stress Tests ◽

Medical Statistics ◽

Neural Network Models ◽

Artificial Neural

Diagnosing the existence of heart disease is really tedious process, as it entails deep knowledge and opulent experience. As a whole, the forecast of heart disease lies upon the conventional method of analysing medical report such as ECG (The Electrocardiogram), MRI (Magnetic Resonance Imaging), Blood Pressure, Stress tests by a Medicinal expert. Nowadays, a large volume of medical statistics is obtainable in medical industry and turns as a excessive source of forecasting valuable and concealed facts in almost all medical complications. Thus, these facts would really aid the doctors to create exact predictions. The innovative methods of Artificial Neural Network models have also been contributing themselves in yielding the main prediction accuracy over medical statistics. This paper targets to predict the presence of heart disease utilizing Back Propagation MLP (Multilayer Perceptron) of Artificial Neural Network. The proposed ANN design targeted to generate the three outputs Yes (Patient having heart disease), No (Patient not having heart disease), and Hesitant (Patient those who are in between yes and no category).

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

Mobile Information Systems ◽

10.1155/2020/8843115 ◽

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.

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