iGWO-RF: an Improved Grey Wolfed Optimization for Random Forest Hyperparameter Optimization to Identification Breast Cancer

Muryadi, Elvaro Islami and Futri, Irianna and Saputra, Dimas Chaerul Ekty (2024) iGWO-RF: an Improved Grey Wolfed Optimization for Random Forest Hyperparameter Optimization to Identification Breast Cancer. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI), 10 (4). pp. 665-680.

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Abstract

The study focuses on improving the accuracy of breast cancer diagnosis by enhancing the predictive capabilities of a Random Forest model. This is achieved by utilizing an improved Grey Wolf Optimization algorithm for hyperparameter optimization. The main objectives are to enhance early detection, increase diagnostic precision, and reduce computational demands in clinical workflows. The work utilizes the Improved Grey Wolf Optimization (iGWO) algorithm to tune the hyperparameters of a Random Forest (RF) model, thereby improving its accuracy in diagnosing breast cancer. The methodology encompasses several steps, including data preparation, model training using iGWO-enhanced RF, performance evaluation compared to traditional methods, and validation using clinical datasets to confirm the reliability and effectiveness of the approach. The iGWO-RF model demonstrated exceptional performance in diagnosing breast cancer, achieving an accuracy of 96.4%, precision of 96.4%, recall of 98.0%, F1-score of 97.2%, and ROC-AUC of 0.988. The findings of iGWO-RF outperform those of SVM, original RF, Naive Bayes, and KNN models, indicating that iGWO-RF is effective in optimizing hyperparameters to improve prediction accuracy. The iGWO-RF model greatly enhances the accuracy and efficiency of breast cancer diagnosis, surpassing conventional models. Integrating iGWO-RF into clinical workflows is advised to improve early identification and patient outcomes. Additional investigation should focus on the utilization of this technology in various medical datasets and circumstances, highlighting its potential in a wide range of healthcare environments.

Item Type:	Artikel Umum
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisi / Prodi:	Faculty of Industrial Technology (Fakultas Teknologi Industri) > S1-Electrical Engineering (S1-Teknik Elektro)
Depositing User:	M.Eng. Alfian Ma'arif
Date Deposited:	05 Feb 2025 02:52
Last Modified:	05 Feb 2025 02:52
URI:	http://eprints.uad.ac.id/id/eprint/79641

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