Performance comparison of feature selection methods for prediction in medical data

Mohd Khalid, Nur Hidayah and Ismail, Amelia Ritahani and Abdul Aziz, Normaziah and Amir Hussin, Amir 'Aatieff (2023) Performance comparison of feature selection methods for prediction in medical data. In: 7th International Conference on Soft Computing in Data Science, SCDS 2023, 24-25 January 2023, Virtual Event.

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Abstract

Along with technological advancement, the application of machine learning algorithms in industry, notably in the medical field, has grown and pro- gressed quickly. Medical databases commonly contain a lot of information about the medical histories of the patients and patient’s conditions, in addition, it is chal- lenging to identify and extract the information that will be relevant and meaning- ful for machine learning modelling. Not to mention, the efficacy of the predictive machine learning algorithm can be enhanced by using only useful and pertinent information. Hence, feature selection is proposed to determine the significant fea- tures. Thus, feature selection should be fully utilized and applied when building machine learning algorithm. This study analyzes filter, wrapper, and embedded feature selection methods for medical data with the predictive machine learn- ing algorithm, Random Forest and CatBoost. The experiment is carried out by evaluating the performances of the machine learning with and without applying feature selection methods. According to the results, CatBoost with RFE shows the best performance, in comparison to Random Forest with other feature selection methods.

Item Type:	Conference or Workshop Item (Other)
Uncontrolled Keywords:	CatBoost, Feature selection, RFE, Lasso
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Amelia Ritahani Ismail
Date Deposited:	03 Aug 2023 16:04
Last Modified:	03 Aug 2023 16:04
URI:	http://irep.iium.edu.my/id/eprint/105807

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