Integrated Framework for Prognosis of Coronary Artery Disease - Rajeswara Rao, D; V.R Murthy, J (International Journal of Engineering and Technology, 2018)
Coronary Artery Disease, commonly known as Heart Disease, has resulted in casualties all over the world. Currently, diagnosis of theseÂ conditions mainly relies on experience of doctors and partly on few decision support systems. Most decision support systems built forÂ this domain have limitations. The major limitation is requirement of huge quantity of historical conditions data and appropriate related diagnosis labels. Proposed framework addresses this limitation by providing diagnosisÂ comparable with existing systems while only taking available data in absence of labels.Â This is achieved by forming a novel framework using Self Organizing Networks andÂ Learning Vector Quantization. Here, competitive learning paradigm of softÂ computing is primary focus. Proposed framework is compared with four well establishedÂ existing systems namely Support Vector Machine, Decision Tree, Random Forest and Multi Layer Perceptron. After comparison and analysis it has been proved that proposed framework gives comparable results even without supplying labeled data.In future this framework can be extended to variegated applications in various domains.Â Â