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.

Richa Gupta

Mansi Bhandari

Anhad Grover

Taher Al-shehari

Mohammed Kadrie

Taha Alfakih

Hussain Alsalman

BioData Mining

BIODATA MINING 是一本开放获取、开放的同行评审期刊，涵盖应用于高维生物和生物医学数据的数据挖掘各个方面的研究，重点关注从大规模遗传、转录组、基因组、蛋白质组和代谢组学数据。主题领域包括但不限于：-新型数据挖掘和机器学习算法的开发、评估和应用。-传统数据挖掘和机器学习算法的适应、评估和应用。-开源软件用于数据挖掘和机器学习算法的应用。-数据库、软件和网络服务的设计、开发和集成，用于大规模研究数据的存储、管理、检索和分析。-预处理、后处理、数据挖掘和机器学习结果的建模和解释，用于生物解释和知识发现。

BioData Mining is an open-access, peer-reviewed journal that focuses on all aspects of data mining applied to high-dimensional biological and biomedical data. The journal covers topics including but not limited to: the development, evaluation, and application of novel data mining and machine learning algorithms; the adaptation, evaluation, and application of traditional data mining and machine learning algorithms; the application of open-source software for data mining and machine learning; the design, development, and integration of databases, software, and web services for the storage, management, retrieval, and analysis of large-scale research data; and the modeling and interpretation of preprocessing, postprocessing, data mining, and machine learning results for biological interpretation and knowledge discovery.

《BioData Mining》是一本开放获取的同行评审期刊，专注于高维生物和生物医学数据的数据挖掘研究。期刊涵盖的主题包括：新型数据挖掘和机器学习算法的开发、评估与应用；传统数据挖掘和机器学习算法的适应、评估与应用；开源软件在数据挖掘和机器学习中的应用；数据库、软件和网络服务的设计、开发与集成，以支持大规模研究数据的存储、管理、检索与分析；以及数据的预处理、后处理、挖掘和机器学习结果的建模与解释，以促进生物学解释和知识发现。

Category	Quartile
生物学	3
生物学, 数学与计算生物学	2

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

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Journal Info

Category	Quartile
MATHEMATICAL & COMPUTATIONAL BIOLOGY	1