Machine Learning Approaches and Applications in Genome Wide Association Study for Alzheimer’s Disease: A Systematic Review
Abbas Saad Alatrany, Abir Hussain, Jamila Mustafina, Dhiya Al‐Jumeily
Abstract
Machine learning algorithms have been used for the detection (and possibly) prediction of Alzheimer’s disease using genotype information, with the potential to enhance the outcome prediction. However, detailed research about the analysis and the detection of Alzheimer’s disease using genetic data is still in its primitive stage. The aim of this paper is to examine the scientific literature on the use of various machine learning approaches for the prediction of Alzheimer’s disease based solely on genetic data. To identify gaps in the literature, critically appraise the reporting and methods of the algorithms, and provide the foundation for a wider research programme focused on developing novel machine learning based predictive algorithms in Alzheimer’s disease. In our study between January 1, 2010, until September 21, 2021, we have reviewed different articles within PubMed, Web of Science, and Scopus to research into keywords and phrases linked to Alzheimer’s disease and machine learning tools, including Artificial Neural Networks, boosting, and random forests. Articles were reviewed for inclusion, then retrieved, and assessed for risk of bias using Preferred Reporting Items for Systematic Reviews and Meta-analyses criteria. A pool of 150 abstracts, 65 full texts was evaluated and 24 studies were considered in the review. Machine learning methods in the reviewed papers performed in a wide range of ways (0.59 to 0.98 AUC). Our study indicated that high risk of bias in the analysis can be linked to feature selection, hyperparameter search and validation methods.