## 5 Conclusions  
We developed a novel MCI conversion prediction framework on the basis of the 3D-ZM and ISPSO algorithms in this paper. In the first stage, a 3D-ZM algorithm was applied to extract the statistical features of 3D-sMRI images. At this stage, a total of 372 statistical features were extracted. Then, we developed a novel feature selection technique based on the IDPSO algorithm to find optimal features for MCI conversion prediction. We assessed the reliability of our MCI conversion prediction framework on 121 sMCI and 105 pMCI patients from the ADNI dataset. Experimental results showed that the proposed method has a strong ability to identify pMCI patients from sMCI patients, which can be useful for determining possible treatment trajectories.  
Conceptualization, P.B. and I.B.; methodology, P.B. and M.G.; software, P.B. and M.G.; validation, P.B., M.G. and M.M.; formal analysis, P.B. and M.G.; writing--original draft preparation, P.B., M.G. and M.M.; writing--review and editing, P.B., M.G. and I.B.; visualization, P.B. and M.G.; supervision, I.B and H.D. All authors have read and agreed to the published version of the manuscript.  
This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.  
Not applicable.  
Data Availability StatementData used in this article were obtained from Alzheimer's Disease Neuroimaging Initiative (ADNI; [[https://adni.loni.usc.edu/data-samples/access-data/](https://adni.loni.usc.edu/data-samples/access-data/)]([https://adni.loni.usc.edu/data-samples/access-data/](https://adni.loni.usc.edu/data-samples/access-data/))).  
Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI), USA (National Institutes of Health Grant U01 AG024904), and the DOD ADNI, USA (Department of Defense award number W81 XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer's Association; Alzheimer's Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd. and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research and Development, LLC.; Johnson and Johnson Pharmaceutical Research and Development LLC.; Lumosity; Lundbeck; Merckand Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institute of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education and the study is coordinated by the Alzheimer's Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.  
On behalf of all authors, the corresponding author states that there is no conflict of interest.