Predictive modeling of ALS progression: an XGBoost approach using clinical features.

IF 4 3区生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY

Biodata Mining Pub Date : 2024-12-02 DOI:10.1186/s13040-024-00399-5

Richa Gupta, Mansi Bhandari, Anhad Grover, Taher Al-Shehari, Mohammed Kadrie, Taha Alfakih, Hussain Alsalman

引用次数: 0

Abstract

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.

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ALS进展的预测建模：使用临床特征的XGBoost方法

本研究提出了一种预测模型，旨在基于从50例患者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。重要的特征包括言语、活动能力和呼吸功能的评估。我们利用XGBoost回归模型预测ALS功能评定量表（ALSFRS-R）的得分，得到训练均方误差（MSE）为0.1651，检验均方误差（MSE）为0.0073，其中训练均方误差为0.9800，检验均方误差为0.9993。该模型具有较高的准确性，为临床医生跟踪疾病进展，提高患者管理和治疗策略提供了有用的工具。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-

CiteScore

7.90

自引率

0.00%

发文量

审稿时长

23 weeks

期刊介绍： BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.