SVM Optimization Based on PSO and AdaBoost to Increasing Accuracy of CKD Diagnosis

Abstract

Classification is data mining techniques which used for the purposes of diagnosis in the medical field as measured by the high accuracy produced. The accuracy of classification algorithm is influenced by the use of features and dimensions in dataset. In this study, Chronic Kidney Disease (CKD) dataset was used where the data is one of the high dimension datasets. Support Vector Machine (SVM) algorithm is used because its ability to handle high-dimensional data. In the dataset, it consists of 24 attributes and 1 class which if all are used results accuracy of classification will be diminished. Method for selecting features with Particle Swarm Optimization (PSO) is applied to reduce redundant features and produce optimal features. In addition, ensemble AdaBoost also applied in this research to increase performance of entirety classification algorithm. The results showed that the optimization of SVM algorithm by using PSO as a selection and ensemble feature of AdaBoost with an average of selected features of 18 features could increase the accuracy of 36.20% to 99.50% in the diagnosis of CKD compared to the SVM algorithm without optimization only resulting in accuracy 63.30%. This research can be used as a reference for further research in focusing on the preprocessing stage.

A Comprehensive Analysis on Detecting Chronic Kidney Disease by Employing Machine Learning Algorithms

“…In addition, Amanah et al has implied PSO algorithms to optimize their result more precisely and has obtained an accuracy of 99.5%. After applying AdaBoost and PSO feature selection algorithm combined, they were able to increase their average accuracy by 36.20% [36]. On the other hand, Chittora et.…”

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“…Dengan P adalah pivot point, H1 adalah harga high kemarin, L1 adalah harga low kemarin, R1 adalah batas Resistance 1 periode sebelumnya dan R2 adalah batas Resistance 2 periode sebelumnya. Rumus dalam menentukan batas support menggunakan persamaan (4) dan (5).…”

“…Penelitian terkait yang memprakarsai penelitian ini dirancang dan dibuat sebagaimana mestinya [2][3][4] [5] yang dimana meskipun konteks yang diteliti berbeda dari penelitian ini, penelitian terkait memiliki tujuan utama yang sama dengan penelitian ini, yaitu melakukan optimalisasi suatu sistem dengan menggunakan metode/algoritma yang sudah dirancang agar kinerja maupun alur dari sistem tersebut lebih efisien dari sebelumnya.…”