Reading: Cardiology Predictor: Cardiology Interpretations for Medical Diagnosis

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Cardiology Predictor: Cardiology Interpretations for Medical Diagnosis

Authors:

Dinithi Nallaperuma ,

BSc(Hons) Informatics Institute of Technology, 57, Ramakrishna Road, Colombo 6,, LK
About Dinithi
Software Engineer
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Kulari Lokuge

MBA, BSc(Hons), Dip.VET, Cert IV TAA, Dip (Computer System Design) Informatics Institute of Technology, 57, Ramakrishna Road, Colombo 6,, LK
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Abstract

Diagnosis of a heart disease is complex. Aspects such as clinical details, blood pressure, pulse rate, cholesterol level and blood test reports, and even patient‟s history, gender and age all are important when diagnosing cardiovascular disease. Interpretation of an Elector Cardiogram (ECG) is one of a key skill developed by cardiologists. Difficulties in analysing complicated ECG may lead to inaccurate diagnosis thus affecting the accuracy and quality with the diagnosis of cardiovascular disease especially among novice.

“Cardiology Predictor” is a software system which is capable of assisting medical practitioners to diagnose cardiovascular diseases accurately. The inputs to the system would be ECG and other cardiac factors which would be processed to provide an output of the possible cardiovascular disease. To ensure accuracy and efficiency, ECG signals are used with digital signal processing for decrypting and feature extracting. Furthermore, artificial intelligence is used to diagnose cardiovascular diseases due to the complexity embedded into it. Therefore, an Artificial Neural Network was used to predict cardiovascular disease. The average success rate of the system was 85.6% based on the user evaluation. Domain experts such as cardiologists suggested that the system is most suitable for the emergency room where expert knowledge was not readily available.

DOI: http://dx.doi.org/10.4038/sljbmi.v2i4.2249

How to Cite: Nallaperuma, D. & Lokuge, K., (2012). Cardiology Predictor: Cardiology Interpretations for Medical Diagnosis. Sri Lanka Journal of Bio-Medical Informatics. 2(4), pp.144–155. DOI: http://doi.org/10.4038/sljbmi.v2i4.2249
Published on 07 Jun 2012.
Peer Reviewed

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