# Assessment for Alzheimer's Disease Advancement Using Classification Models with Rules
## 3 The Algorithm
### Learning Phase
When age and education level were combined with cognitive items, the classification models derived by the machine learning algorithms improved. In the near future, we will study specific dementia subgroups for 3-5 years to analyse if unique cognitive features for each sub-group can be found. Conceptualization, F.T. and D.P. ; Methodology, F.T. ; Formal analysis, F.T. ; Investigation, F.T. ; Writing--original draft, F.T. and D.P. ; Writing--review and editing, F.T. and D.P. ; Supervision, D.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Data Availability Statement: Data used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI was launched in 2003 as a public-private partnership led by Prin-cipal Investigator [PERSON], MD. The primary goal of ADNI has been to test whether serial magnetic res-onance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological-chological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer's disease (AD). For up-to-date information, see www.adni-info.org for further details on data. Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81 XWH-12-2-0012). The authors declare no conflict of interest. ## References

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