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A Bi-objective Hybrid Optimization Algorithm to Reduce Noise and Data Dimension in Diabetes Diagnosis Using Support Vector Machines by Mahsa Alirezaei; Seyed Taghi Akhavan Niaki; Seyed Armin Akhavan Niaki is a scholarly article available to read on EtoBox.

What is A Bi-objective Hybrid Optimization Algorithm to Reduce Noise and Data Dimension in Diabetes Diagnosis Using Support Vector Machines about?

Diabetes mellitus is a medical condition examined by data miners for reasons such as significant health complications in affected people, the economic impact on healthcare networks, and so on. In order to find the main causes of this disease, researchers look into the patient's lifestyle, hereditary information, etc. The goal of data mining in this context is to find patterns that make early detection of the disease and proper treatment easier. Due to the high volume of data involved in therapeutic contexts and disease diagnosis, provision of the intended treatment method become almost impossible over a short period of time. This justifies the use of preprocessing techniques and data reduction methods in such contexts. In this regard, clustering and meta-heuristic algorithms maintain important roles. In this paper, a method based on the k-means clustering algorithm is first utilized to detect and delete outliers. Then, in order to select significant and effective features, four bi-objective meta-heuristic algorithms are employed to choose the least number of significant features with the highest classification accuracy using support vector machines (SVM). In addition, the 10-fold c

Author

Mahsa Alirezaei; Seyed Taghi Akhavan Niaki; Seyed Armin Akhavan Niaki

Publisher

Elsevier BV

Published

2019

Language

EN