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All Journal Buletin Ilmiah Sarjana Teknik Elektro Proceeding ISETH (International Summit on Science, Technology, and Humanity)
Tun, Su Sandi Hla
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Computer Science & IT Electrical & Electronics Engineering Engineering Medicine & Pharmacology Social Sciences Other

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Influential Factors Contributing to Stroke Recovery: A Review Tun, Su Sandi Hla; Wanpen, Sawitri; Nualnetr, Nomjit
Proceeding ISETH (International Summit on Science, Technology, and Humanity) 2023: Proceeding ISETH (International Summit on Science, Technology, and Humanity)
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/iseth.4222

Abstract

Stroke is a major public health problem affecting disability to people after stroke. Deficits in motor function resulting from stroke can impact the mobility of patient, ability to perform tasks in everyday life, social interaction, and likelihood of resuming work. The recovery of stroke is important for the survivors to return to daily life. Several factors influence the stroke recovery and can predict the conditions of the patients to obtain the optimal rehabilitative therapeutics individually. The aim of the paper is to evaluate the influential factors contributing to stroke recovery. When the clinicians from rehabilitation teams have a better understanding of these individualized factors, the therapeutic goals can become holistic, effective and realistic for the patients to achieve the maximal motor function recovery after stroke.
HNIHA: Hybrid Nature-Inspired Imbalance Handling Algorithm to Addressing Imbalanced Datasets for Improved Classification: In Case of Anemia Identification Saputra, Dimas Chaerul Ekty; Ratnaningsih, Tri; Futri, Irianna; Muryadi, Elvaro Islami; Phann, Raksmey; Tun, Su Sandi Hla; Caibigan, Ritchie Natuan
Buletin Ilmiah Sarjana Teknik Elektro Vol. 6 No. 3 (2024): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/biste.v6i3.11306

Abstract

This study presents a comprehensive evaluation of three ensemble models designed to handle imbalanced datasets. Each model incorporates the hybrid nature-inspired imbalance handling algorithm (HNIHA) with matthews correlation coefficient and synthetic minority oversampling technique in conjunction with different base classifiers: support vector machine, random forest, and LightGBM. Our focus is to address the challenges posed by imbalanced datasets, emphasizing the balance between sensitivity and specificity. The HNIHA algorithm-guided support vector machine ensemble demonstrated superior performance, achieving an impressive matthews correlation coefficient of 0.8739, showcasing its robustness in balancing true positives and true negatives. The f1-score, precision, and recall metrics further validated its accuracy, precision, and sensitivity, attaining values of 0.9767, 0.9545, and 1.0, respectively. The ensemble demonstrated its ability to minimize prediction errors by minimizing the mean squared error and root mean squared error to 0.0384 and 0.1961, respectively. The HNIHA-guided random forest ensemble and HNIHA-guided LightGBM ensemble also exhibited strong performances.
Co-Authors Caibigan, Ritchie Natuan Dimas Chaerul Ekty Saputra Futri, Irianna Muryadi, Elvaro Islami Nualnetr, Nomjit Phann, Raksmey Tri Ratnaningsih Wanpen, Sawitri