Gupta Richa, Bhandari Mansi, Grover Anhad, Al-Shehari Taher, Kadrie Mohammed, Alfakih Taha, Alsalman Hussain
Department of Computer Science and Engineering, School of Engineering Sciences and Technology, Jamia Hamdard, Delhi, India.
Computer Skills, Department of Self-Development Skill, Common First Year Deanship, King Saud University, Riyadhi, 11362, Saudi Arabia.
BioData Min. 2024 Dec 2;17(1):54. doi: 10.1186/s13040-024-00399-5.
This research presents a predictive model aimed at estimating the progression of Amyotrophic Lateral Sclerosis (ALS) based on clinical features collected from a dataset of 50 patients. Important features included evaluations of speech, mobility, and respiratory function. We utilized an XGBoost regression model to forecast scores on the ALS Functional Rating Scale (ALSFRS-R), achieving a training mean squared error (MSE) of 0.1651 and a testing MSE of 0.0073, with R² values of 0.9800 for training and 0.9993 for testing. The model demonstrates high accuracy, providing a useful tool for clinicians to track disease progression and enhance patient management and treatment strategies.
本研究提出了一种预测模型，旨在根据从50名患者的数据集中收集的临床特征来估计肌萎缩侧索硬化症（ALS）的进展。重要特征包括言语、运动和呼吸功能评估。我们使用XGBoost回归模型来预测ALS功能评定量表（ALSFRS-R）的得分，训练均方误差（MSE）为0.1651，测试MSE为0.0073，训练的R²值为0.9800，测试的R²值为0.9993。该模型显示出很高的准确性，为临床医生跟踪疾病进展以及加强患者管理和治疗策略提供了一个有用的工具。
