Distributed Healthcare Framework Using MMSM-SVM and P-SVM Classification

作     者：R.Sujitha B.Paramasivan 

作者机构：Department of Information TechnologyNational Engineering College(Autonomous)Kovilpatti628503TamilnaduIndia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第1期

页      面：1557-1572页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This study is supported by the Tamil Nadu State Council of Science and Technology 

主　　题：Lung cancer COVID-19 machine learning deep learning parallel based support vector machine multiclass-based multiple submodel 

摘      要：With the modernization of machine learning techniques in healthcare,different innovations including support vector machine(SVM)have predominantly played a major role in classifying lung cancer,predicting coronavirus disease 2019,and other *** particular,our algorithm focuses on integrated datasets as compared with other existing *** this study,parallel-based SVM(P-SVM)andmulticlass-basedmultiple submodels(MMSM-SVM)were used to analyze the optimal classification of lung *** analysis aimed to find the optimal classification of lung diseases with id and stages,such as key-value pairs in MapReduce combined with P-SVM and MMSVM for binary and multiclasses,*** nonlinear classification,kernel clustering-based SVM embedded with multiple submodels was *** algorithms were developed using Apache spark environment,and data for the analysis were retrieved from microscope lab,UCI,Kaggle,and General Thoracic surgery database along with some electronic health records related to various lung diseases to increase the dataset size to 5 *** measures were conducted using a 5 GB dataset with five *** size was finally increased,and task analysis and CPU utilization were measured.