ESVM‐SWRF: Ensemble SVM‐based sample weighted random forests for liver disease classification. (21st September 2021)

• Record Type: Journal Article
• Authors: Padmakala, S.; Subasini, C. A.; Karuppiah, S. P.; Sheeba, Adlin
• Is Part Of: International journal for numerical methods in biomedical engineering. Volume 37:Number 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-21
• Subjects: ensemble SVM -- liver disease prediction -- novel improved colliding boding optimization -- sample weighted random forest; Biomedical engineering -- Periodicals; Imaging systems in medicine -- Periodicals; Numerical analysis -- Periodicals; Engineering mathematics -- Periodicals; 610.28
• DOI: 10.1002/cnm.3525
• Languages: English
• ISSNs: 2040-7939

Abstract

Abstract: Recently, a significant way to diagnose the disease is using the model of medical data mining. The most challenging task in the healthcare field is to face a large amount of data during disease analyzes and prediction. Once the data are transformed into valuable data by means of data mining models then the actual prediction and decision making is easier. The existing studies met few shortcomings because of higher execution time, more computational complexities, less scalability, slow convergence, and lack of providing the solution. In this article, we have proposed an ensemble SVM‐based sample weighted random forests ( eSVM‐swRF ) with novel improved colliding body optimization (NICBO) algorithm to predict liver diseases. The extraction, loading, transformation, and analysis (ELTA) are used to pre‐process the patient data. The significant feature with a suitable model is generated depending upon the filter‐based method. Based on eSVM‐swRF, the parameter values such as penalty parameter ( P ), threshold ( T ), and mTry are optimized via a novel improved colliding boding optimization (NICBO) algorithm. The UCI dataset provides liver disease data for this study. The implementation platform of RapidMiner Studio version 7.6 with different evaluation measures is used to validate the performance of eSVM‐swRF with the NICBO method. Anyway, the proposed method yields outstanding performance than other existing methods such as Particle Swarm Optimization‐based Support Vector Machine (PSO‐SVM), fuzzy adaptive, and neighbor weighted k‐NN (FuzzyANWKNN), Naïve Bayes‐based Support Vector Machine (NB‐SVM), and Neural network. Abstract : The UCI dataset provides liver disease data for this study. Using eSVM‐swRF with NICBO algorithm thereby increasing AUC value. The experimental data are obtained from the UCI repository.