An Approach to ECG-based Gender Recognition Using Random Forest Algorithm

Nuuruddin Hamid Arif; Mohammad Reza Faisal; Andi Farmadi; Dodon Nugrahadi; Friska Abadi; Umar Ali Ahmad

doi:10.35882/jeeemi.v6i2.363

Nuuruddin Hamid Arif Computer Science Department, Lambung Mangkurat University, Banjarbaru, Indonesia https://orcid.org/0000-0001-5748-7639
Mohammad Reza Faisal Computer Science Department, Lambung Mangkurat University, Banjarbaru, Indonesia https://orcid.org/0000-0001-5748-7639
Andi Farmadi Computer Science Department, Lambung Mangkurat University, Banjarbaru, Indonesia https://orcid.org/0000-0001-5748-7639
Dodon Nugrahadi Computer Science Department, Lambung Mangkurat University, Banjarbaru, Indonesia https://orcid.org/0000-0001-5748-7639
Friska Abadi Computer Science Department, Lambung Mangkurat University, Banjarbaru, Indonesia https://orcid.org/0000-0001-5748-7639
Umar Ali Ahmad Collaborative Researcher, Kanazawa University, Kanazawa, Ishikawa, Japan; School of Electrical Engineering, Telkom University, Bandung, Indonesia https://orcid.org/0000-0001-9285-297X

DOI: https://doi.org/10.35882/jeeemi.v6i2.363

Keywords: random forest, gender classification, ECG

Abstract

Human-Computer Interaction (HCI) has witnessed rapid advancements in signal processing research within the health domain, particularly in signal analyses like electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG). ECG, containing diverse information about medical history, identity, emotional state, age, and gender, has exhibited potential for biometric recognition. The Random Forest method proves essential to facilitate gender classification based on ECG. This research delves into applying the Random Forest method for gender classification, utilizing ECG data from the ECG ID Database. The primary aim is to assess the efficacy of the Random Forest algorithm in gender classification. The dataset employed in this study comprises 10,000 features, encompassing both raw and filtered datasets, evaluated through 10-fold cross-validation with Random Forest Classification. Results reveal the highest accuracy for raw data at 55.000%, with sensitivity at 46.452% and specificity at 63.548%. In contrast, the filtered data achieved the highest accuracy of 65.806%, with sensitivity and specificity at 67.097%. These findings conclude that the most significant impact on gender classification in this study lies in the low sensitivity value in raw data. The implications of this research contribute to knowledge by presenting the performance results of the Random Forest algorithm in ECG-based gender classification.

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