Early Detection and Prediction Of Coronary Artery Disease
Machine Learning method, Coronary Artery Disease, Medical Imaging, Data pre-processing, Logistic regression, accuracy score, data analysis and visualization, Random Forest classifier, KNN algorithm
As the millions of Coronary Artery Disease people have been observed worldwide over the past years. Coronary Artery Disease (CAD) prediction is a very hard and challenging task in the medical field. The early prediction in the medical field especially the cardiovascular sector is one of the virtuosi. The prior studies about the construction of the early prediction model developed an understanding of the recent techniques to find the variation in medical imaging. The prevention of cardiovascular can be fulfilled through a diet chart prepared by the concerned physician after early prediction. Our research paper consists of the prediction of CAD by the proposed algorithm by constructing of pooled area curve (PUC) in the machine learning method.Data pre-processing helps to format the data into useful form by removing redundancy and noise, eliminating missing and non-numerical values, and also by normalization. Data analysis and visualization are carried out to improve the statistical analysis of given data. Logistic regression is carried out on the data since it contains lot of columns with categorical values. Accuracy of the model have been measured. Various conclusions can be drawn from this interdependent data set and can be stored as historical data for future analysis. We then try out various other ML algorithms like Random Forest classifier, SVM and KNN algorithm. We then compare the models with Logistic Regression method.
Article Details
Unique Paper ID: 154675

Publication Volume & Issue: Volume 8, Issue 12

Page(s): 40 - 43
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