**6. Conclusions**

Having techniques that can determine if an individual has Alzheimer disease is likely going to become increasingly important. This area of research has, arguably, not received enough attention in the past. This is probably due to the fact that there was no treatment available. This has recently changed, with the FDA approving [46–49] the first drug for the treatment of Alzheimer disease (there were drugs before targeting some of the effects of the illness but not the actual illness itself).

The results, for instance, in Table 9, sugges<sup>t</sup> that the approached followed can generate an accurate forecast (out-of-sample), when using a multi dataset approach, which is a significant development, with, for instance, the sensitivity and the specificity reaching, respectively, 0.9007 and 0.9485 values, when using 4300 CpGs. The obtained positive predictive value (PPV) and the negative predictive value (NPV) were also relatively high, coming in at 0.9621 and 0.8679, respectively. The results also indicate (Figures 1 and 2) that

increasing the number of CpGs does not improve the forecast. This is very likely related to the issue of local minima.

It is also important to remark that, as more data becomes available, the algorithm could be used to classify between healthy and AD patients following a less invasive approach. Most of the currently available methylation data are related to brain tissue that requires an invasive procedure to be obtained. However, methylation datasets in numerous other illnesses already exist, using blood. As blood-based datasets become available, the algorithm presented in this paper can be easily applied to those, potentially becoming an additional practical tool for diagnosis of the illness. There are also several interesting lines of future work. For instance, the addition of new datasets as they become gradually available.

#### **Supplementary Materials:** The following are available online at https://www.mdpi.com/2227-739 0/9/19/2482/s1.

**Author Contributions:** Conceptualization, G.A.P.; methodology, G.A.P. and J.C.V.; software, G.A.P.; validation, G.A.P. and J.C.V.; formal analysis, G.A.P. and J.C.V.; investigation, G.A.P. and J.C.V.; resources, G.A.P. and J.C.V.; data curation, G.A.P. and J.C.V.; writing—original draft preparation, G.A.P.; writing—review and editing, G.A.P. and J.C.V.; visualization, G.A.P. and J.C.V.; supervision, G.A.P. and J.C.V.; project administration, G.A.P. and J.C.V.; funding acquisition, G.A.P. and J.C.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Data Availability Statement:** All the data used in this paper is publicly available at the GEO Database (https://www.ncbi.nlm.nih.gov/geo/, accessed on 1 July 2021).

**Conflicts of Interest:** The authors declare no conflict of interest.
