Alzheimer's Disease (AD) ravages the cognitive ability of more than 5 million Americans and creates an enormous strain on the health care system. This paper proposes a machine learning predictive model for AD development without medical imaging and with fewer clinical visits and tests, in hopes of earlier and cheaper diagnoses. That earlier diagnoses could be critical in the effectiveness of any drug or medical treatment to cure this disease. Our model is trained and validated using demographic, biomarker and cognitive test data from two prominent research studies: Alzheimer's Disease Neuroimaging Initiative (ADNI) and Australian Imaging, Biomarker Lifestyle Flagship Study of Aging (AIBL). We systematically explore different machine learning models, pre-processing methods and feature selection techniques. The most performant model demonstrates greater than 90% accuracy and recall in predicting AD, and the results generalize across sub-studies of ADNI and to the independent AIBL study. We also demonstrate that these results are robust to reducing the number of clinical visits or tests per visit. Using a metaclassification algorithm and longitudinal data analysis we are able to produce a "lean" diagnostic protocol with only 3 tests and 4 clinical visits that can predict Alzheimer's development with 87% accuracy and 79% recall. This novel work can be adapted into a practical early diagnostic tool for predicting the development of Alzheimer's that maximizes accuracy while minimizing the number of necessary diagnostic tests and clinical visits.
Longitudinal Bayesian Learning of Continuous Disease Position across the Alzheimer's Disease Continuum
University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；University of Pittsburgh（匹兹堡大学）；Eli and Lilly Company（礼来公司）；Washington University in St. Louis（圣路易斯华盛顿大学）
Towards early diagnosis of Alzheimer's disease: Advances in immune-related blood biomarkers and computational modeling approaches
Fraunhofer Institute for Algorithms and Scientific Computing (SCAI)（弗劳恩霍夫算法与科学计算研究所（SCAI））；University of Bonn（波恩大学）；Tel Aviv University（特拉维夫大学）；Hospital Clínic de Barcelona（巴塞罗那临床医院）；University of Barcelona（巴塞罗那大学）；ImmunoBrain Checkpoint Ltd.（ImmunoBrain Checkpoint有限公司）；Sagol School of Neuroscience（萨戈尔神经科学学院）
Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset
