Novel Ensemble Learning Algorithm for Early Detection of Lower Back Pain Using Spinal Anomalies
Moin Haider,
Muhammad Shadab Alam Hashmi,
Ali Raza,
Muhammad Ibrahim,
Norma Latif Fitriyani (),
Muhammad Syafrudin () and
Seung Won Lee ()
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Moin Haider: Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan
Muhammad Shadab Alam Hashmi: Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan
Ali Raza: Department of Software Engineering, University of Lahore, Lahore 54000, Pakistan
Muhammad Ibrahim: Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan
Norma Latif Fitriyani: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea
Muhammad Syafrudin: Department of Artificial Intelligence and Data Science, Sejong University, Seoul 05006, Republic of Korea
Seung Won Lee: Department of Precision Medicine, School of Medicine, Sungkyunkwan University, Suwon 16419, Republic of Korea
Mathematics, 2024, vol. 12, issue 13, 1-18
Abstract:
Lower back pain (LBP) is a musculoskeletal condition that affects millions of people worldwide and significantly limits their mobility and daily activities. Appropriate ergonomics and exercise are crucial preventive measures that play a vital role in managing and reducing the risk of LBP. Individuals with LBP often exhibit spinal anomalies, which can serve as valuable indicators for early diagnosis. We propose an advanced machine learning methodology for LBP detection that incorporates data balancing and bootstrapping techniques. Leveraging the features associated with spinal anomalies, our method offers a promising approach for the early detection of LBP. Our study utilizes a standard dataset comprising 310 patient records, including spinal anomaly features. We propose an ensemble method called the random forest gradient boosting XGBoost Ensemble (RGXE), which integrates the combined power of the random forest, gradient boosting, and XGBoost methods for LBP detection. Experimental results demonstrate that the proposed ensemble method, RGXE Voting, outperforms state-of-the-art methods, achieving a high accuracy of 0.99. We fine-tuned each method and validated its performance using k-fold cross-validation in addition to determining the computational complexity of the methods. This innovative research holds significant potential to revolutionize the early detection of LBP, thereby improving the quality of life.
Keywords: lower back pain; healthcare; artificial intelligence; mathematical modeling; ensemble learning; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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