Volume 11 - Volume 11
Clinical Decision Support Systems Using Sequential Pattern Mining Algorithms for Cardio Vascular Diseases
Abstract
In the medical field, Cardio Vascular disease (CVD) considered to be the treacherous diseases in all
aspects also it leads to the stroke, heart attack, angina (chest torment) etc Generally the classifier used
to analyze and foretell the disease are Support Vector Machine Classifier (SVMC), Logistic Regression
Classifier (LRC), Random Forest Classifier (RFC), Decision Tree Classifier (DTC) and K-nearest
neighbours Classifier (KNNC). While diagnose in this way, classifier may misclassify due to the vast
amount of data being generated in all the fields and also its time consuming in order to detect some
hard disease in early stage. So we propose an improved sequential pattern mining algorithm (Two
phase) combined with association pattern mining (APM) method. Here we grouped data’s based on
their similarity of symptoms also examined by use of discriminant analysis (DA) to check the grouping
significance. Then we build a clinical decision support system by using sequential pattern mining along with association pattern mining. Then the results are compared by using evaluation metrics.
Paper Details
PaperID: 1973
Author's Name: Cherukuri Harini and Dr.V. Maria Anu
Volume: Volume 11
Issues: Volume 11
Keywords: Cardio Vascular Disease, Machine Learning, Sequential Pattern Mining, Association Pattern Mining.
Year: 2021
Month: May
Pages: 756-770