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

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

Author : P. Umasankar and V. Thiagarasu
Volume 8 No.1 January-March 2019 pp 1-8

Abstract

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).

Keywords

Artificial Neural Network, Multi-Layer Perceptron, Heart Disease

Full Text:

References

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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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Department of Environmental Toxicology, Texas Tech University, Texas
[email protected]
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Dr. Kamarul Ariffin Bin Noordin
Department of Electrical Engineering, University of Malaya, Malaysia
[email protected]
Dr. Benjamin T.F. Chung
Department of Mechanical Engineering, University of Akron, Akron, USA
[email protected]
Dr. Mohd Faiz Bin Mohd Salleh
Department of Electrical Engineering, University of Malaya, Malaysia
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Dr. Suhana Binti Mohd Said
Department of Electrical Engineering, University of Malaya, Malaysia
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Dr. Norrima Binti Mokhtar
Department of Electrical Engineering, University of Malaya, Malaysia
[email protected]
Dr. Mohamadariff
Department of Electrical Engineering, University of Malaya, Malaysia
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    Editorial Note

    Editorial Dr. Seshadri Ramkumar

    Editor-in-Chief
    Dr. Seshadri Ramkumar
    Department of Environmental Toxicology, Texas Tech University, Texas
    [email protected]
    Editorial Advisory Board
    Dr. Kamarul Ariffin Bin Noordin
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Benjamin T.F. Chung
    Department of Mechanical Engineering, University of Akron, Akron, USA
    [email protected]
    Dr. Mohd Faiz Bin Mohd Salleh
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Suhana Binti Mohd Said
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Norrima Binti Mokhtar
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Mohamadariff
    Department of Electrical Engineering, University of Malaya, Malaysia
    [email protected]

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