Application of novel hybrid machine learning systems and radiomics features for non-motor outcome prediction in Parkinson’s disease

蒙特利尔认知评估 人工智能 机器学习 特征选择 计算机科学 痴呆 桥接(联网) 多元统计 人口 疾病 医学诊断 医学 内科学 病理 计算机网络 环境卫生
作者
Mohammad R. Salmanpour,Mahya Bakhtiyari,Mahdi Hosseinzadeh,Mehdi Maghsudi,Fereshteh Yousefirizi,Mohammad Mehdi Ghaemi,Arman Rahmim
出处
期刊:Physics in Medicine and Biology [IOP Publishing]
卷期号:68 (3): 035004-035004 被引量:4
标识
DOI:10.1088/1361-6560/acaba6
摘要

Abstract Objectives. Parkinson’s disease (PD) is a complex neurodegenerative disorder, affecting 2%–3% of the elderly population. Montreal Cognitive Assessment (MoCA), a rapid nonmotor screening test, assesses different cognitive dysfunctionality aspects. Early MoCA prediction may facilitate better temporal therapy and disease control. Radiomics features (RF), in addition to clinical features (CF), are indicated to increase clinical diagnoses, etc, bridging between medical imaging procedures and personalized medicine. We investigate the effect of RFs, CFs, and conventional imaging features (CIF) to enhance prediction performance using hybrid machine learning systems (HMLS). Methods. We selected 210 patients with 981 features (CFs, CIFs, and RFs) from the Parkinson’s Progression-Markers-Initiative database. We generated 4 datasets, namely using (i), (ii) year-0 (D1) or year-1 (D2) features, (iii) longitudinal data (D3, putting datasets in years 0 and 1 longitudinally next to each other), and (iv) timeless data (D4, effectively doubling dataset size by listing both datasets from years 0 and 1 separately). First, we directly applied 23 predictor algorithms (PA) to the datasets to predict year-4 MoCA, which PD patients this year have a higher dementia risk. Subsequently, HMLSs, including 14 attribute extraction and 10 feature selection algorithms followed by PAs were employed to enhance prediction performances. 80% of all datapoints were utilized to select the best model based on minimum mean absolute error (MAE) resulting from 5-fold cross-validation. Subsequently, the remaining 20% was used for hold-out testing of the selected models. Results. When applying PAs without ASAs/FEAs to datasets (MoCA outcome range: [11,30]), Adaboost achieved an MAE of 1.74 ± 0.29 on D4 with a hold-out testing performance of 1.71. When employing HMLSs, D4 + Minimum_Redundancy_Maximum_Relevance (MRMR)+K_Nearest_Neighbor Regressor achieved the highest performance of 1.05 ± 0.25 with a hold-out testing performance of 0.57. Conclusion. Our study shows the importance of using larger datasets (timeless), and utilizing optimized HMLSs, for significantly improved prediction of MoCA in PD patients.
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