A Novel Tree-based Feature Selection for Diagnosing Bipolar Disorder, a Real-World Scenario
سال انتشار: 1401
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 129
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شناسه ملی سند علمی:
ITCT15_017
تاریخ نمایه سازی: 3 مرداد 1401
چکیده مقاله:
One of the most common causes of mortality worldwide is Bipolar disorder (BD). Patients with this problem have life expectancy lower than normal people. By applying Machine Learning (ML) techniques (Classification) to the indicators, we are able to reduce prognostic uncertainty associated with the subjective characteristics of BD. Oneimportant method that has a great effect on the overall outcome of the classification methods is Feature Selection. In this paper, a new method of tree-based featureselection is proposed and tested over a real-world data set of patients' indicators. To make the work as accurate as possible the several well-known classification algorithmsare used. The outcome of various performance metrics endorsed the ability of the proposed tree-based feature selection in reducing the classification error related to diagnosing the bipolar disorder.
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نویسندگان
Moeinoddin Sheikhottayefe
Amir Kabir University of Technology