An Automated Text Summarization and Machine Learning-Based Framework for Heart Disease Prediction

Abstract

Heart disease is the primary cause of death of humankind nowadays. Text summarization is currently a major research topic in natural language processing, and it is an important activity in the analysis of high-volume text documents. In the chapter, an automated summarization of text has been implemented using sentence scoring approach, which includes finding the frequent terms and sentence ranking method. This technique mainly focuses on summarizing medical reports. The proposed approach also uses three algorithms, namely SVM (support vector machine), KNN (k-nearest neighbor), and random forest algorithm, for disease prediction. Experimental results are carried out on several data sets, and they show that the proposed approach provides best accuracy compared to traditional techniques.