A hybrid approach to select features and classify diseases based on medical data

2017 International Symposium on Application of Materials Science and Energy Materials

材料科学;能源学

Abdellatif, Hisham^1 ; Luo, Jiawei^1

College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan

410082, China^1

关键词: Classification accuracy;    Classification algorithm;    Classification process;    Clinical medicine;    Hybrid approach;    Hybrid methodologies;    K-means clusters;    Medical data sets;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/322/6/062002/pdf
DOI  :  10.1088/1757-899X/322/6/062002

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Feature selection is popular problem in the classification of diseases in clinical medicine. Here, we developing a hybrid methodology to classify diseases, based on three medical datasets, Arrhythmia, Breast cancer, and Hepatitis datasets. This methodology called k-means ANOVA Support Vector Machine (K-ANOVA-SVM) uses K-means cluster with ANOVA statistical to preprocessing data and selection the significant features, and Support Vector Machines in the classification process. To compare and evaluate the performance, we choice three classification algorithms, decision tree Naïve Bayes, Support Vector Machines and applied the medical datasets direct to these algorithms. Our methodology was a much better classification accuracy is given of 98% in Arrhythmia datasets, 92% in Breast cancer datasets and 88% in Hepatitis datasets, Compare to use the medical data directly with decision tree Naïve Bayes, and Support Vector Machines. Also, the ROC curve and precision with (K-ANOVA-SVM) Achieved best results than other algorithms.