Assessing the Importance of Attributes for Diagnosis of Diabetes Disease

Akyol, Kemal

doi:10.5815/ijieeb.2017.05.01

Cited by 3 publications

(1 citation statement)

References 27 publications

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“…There are many studies on this subject in the literature. For example, Akyol investigated the important attributes for effective diagnosis of liver disorders by performing the machine learning approach based on the combination of Stability Selection and Random Forest methods [29]. Chittineni and Bhogapathi applied the Exhaustive search and Heuristic search techniques in order to determine features that contribute to cluster data [30].…”

Section: Proposed Approach and Experimental Resultsmentioning

confidence: 99%

A Study on Liver Disease Diagnosis based on Assessing the Importance of Attributes

Akyol¹,

Gültepe²

2017

IJISA

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Abstract-Liver is a needful body organ that forms an important barrier between the gastrointestinal blood, which contains large amounts of toxins, and antigens. Liver diseases contain hepatitis B and hepatitis C virus infections, alcoholic liver disease, nonalcoholic fatty liver disease and associated cirrhosis, liver failure and hepatocellular carcinoma are primary causes of death. The main purpose of this study is to investigate which attributes are important for effective diagnosis of liver disorders by performing the machine learning approach based on the combination of Stability Selection and Random Forest methods. In order to generate more accuracy, dataset was balanced by utilizing the Random Under-Sampling method. Important ones in all attributes were detected by utilizing the Stability Selection method which was performed on sub-datasets, which were obtained with 5 fold cross-validation technique. By sending these datasets to the Random Forest algorithm, the performance of the proposed approach was evaluated within the frame of accuracy and sensitive metrics. The experimental results clearly show that the Random Under-Sampling method can potentially improve the performance of the combination of Stability Selection and Random Forest methods in machine learning. And, the combination of these methods provides new perspectives for the diagnosis of this disease and other medical diseases.

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Section: Proposed Approach and Experimental Resultsmentioning

confidence: 99%