Decision Tree Based Salp Swarm Optimization for Multi Medical Data Classification with Feature Reduction Technique

HIGHLIGHTS

• This paper proposes a new hybrid feature selection method.

• This paper proposes a novel Decision Tree based salp optimization algorithm.

• The proposed model is trained using four datasets namely Leukemia, Diffuse Larger B-cell Lymphomas (DLBCL), Lung cancer and Colon.

• This paper produces an accuracy of 98.88.

Abstract

The ambitious task in the domain of medical informatics is medical data classification. From medical datasets, intention to ameliorate human burden with the medical data classification entails to taking in classification designs. The medical data classification is the major focus of this paper, where a Decision Tree based Salp Swarm Optimization (DT-SWO) algorithm is proposed. After pre-processingthe hybrid feature selection method selects the medical data features. The high dimensional features are reduced by Discriminant Independent Component Analysis (DICA) and DT-SWO is to classify the most relevant class of medical data. The details of four datasets namely Leukemia, Diffuse Larger B-cell Lymphomas (DLBCL), Lung cancer and Colon relating to four diseases for heart, liver, cancer and lungs are collected from the UCI machine learning repository. Ultimately, the experimental outcomes demonstrated that the proposed DT-SWO algorithm is suitable for medical data classification than other algorithms.

Keywords:
Medical data; Feature selection; Decision Tree based Salp Swarm Optimization; Discriminant Independent Component Analysis and dataset