Feature selection using metaheuristics to predict annual amyotrophic lateral sclerosis progression.

Thibault Anani, Jean-François Pradat-Peyre, François Delbot, Claude Desnuelle, Anne Sophie Rolland, David Devos, Pierre-François Pradat
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Abstract

Objective: Amyotrophic lateral sclerosis (ALS), a progressive neurodegenerative disease with no curative treatment and affecting motor neurons, leads to motor weakness, atrophy, spasticity and difficulties with speech, swallowing, and breathing. Accurately predicting disease progression and survival is crucial for optimizing patient care, intervention planning, and informed decision-making.

Methods: Data were gathered from the PRO-ACT database (4659 patients), clinical trial data from ExonHit Therapeutics (384 patients) and the PULSE multicenter cohort aimed at identifying predictive factors of disease progression (198 patients). Machine learning (ML) techniques including logistic/linear regression (LR), K-nearest neighbors, decision tree, random forest, and light gradient boosting machine (LGBM) were applied to forecast ALS progression using ALS Functional Rating Scale (ALSFRS) scores and patient survival over one year. Models were validated using 10-fold cross-validation, while Kaplan-Meier estimates were employed to cluster patients according to their profiles. To enhance the predictive accuracy of our models, we performed feature selection using ANOVA and differential evolution (DE).

Results: LR with DE achieved a balanced accuracy of 76.05% on validation (ranging from 68.6% to 79.8% per fold) and 76.33% on test data, with an AUC of 0.84. With Kaplan-Meier's estimates, we identified five distinct patient clusters (C-index = 0.8; log-rank test p value ≤0.0001). Additionally, LGBM predictions for ALSFRS progression at 3 months yielded an RMSE of 3.14 and an adjusted R2 of 0.764.

Conclusion: This study showcases the potential of ML models to provide significant predictive insights in ALS, enhancing the understanding of disease dynamics and supporting patient care.

特征选择使用元启发式预测年度肌萎缩侧索硬化症的进展。
目的:肌萎缩性侧索硬化症(ALS)是一种无法治愈的进行性神经退行性疾病,影响运动神经元,导致运动无力、萎缩、痉挛以及言语、吞咽和呼吸困难。准确预测疾病进展和生存对于优化患者护理、干预计划和知情决策至关重要。方法:数据来自PRO-ACT数据库(4659例患者)、ExonHit Therapeutics的临床试验数据(384例患者)和旨在确定疾病进展预测因素的PULSE多中心队列(198例患者)。机器学习(ML)技术,包括逻辑/线性回归(LR)、k近邻、决策树、随机森林和光梯度增强机(LGBM),应用于使用ALS功能评定量表(ALSFRS)评分预测ALS进展和患者一年以上的生存率。模型使用10倍交叉验证进行验证,而Kaplan-Meier估计则根据患者的概况进行聚类。为了提高模型的预测精度,我们使用方差分析和差分进化(DE)进行特征选择。结果:LR与DE在验证上达到76.05%的平衡准确度(每倍68.6%至79.8%),在测试数据上达到76.33%,AUC为0.84。根据Kaplan-Meier估计,我们确定了五个不同的患者群(C-index = 0.8;Log-rank检验p值≤0.0001)。此外,LGBM预测3个月时ALSFRS进展的RMSE为3.14,调整后的R2为0.764。结论:本研究展示了ML模型在ALS中提供重要预测见解的潜力,增强了对疾病动态的理解并支持患者护理。
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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