Synthetic data analysis for early detection of Alzheimer progression through machine learning algorithms - Supplemental experimental results

Aug 5, 2026

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Supplemental experimental results expand an ensemble-based machine learning workflow for early detection of Alzheimer progression using synthetic datasets. Features are processed through RFE and AIC to obtain feature sets, then integrated into ML models, with comparisons summarized across multiple dataset pairs and experimental phases. Tables S1–S3 report feature selections and subject counts, while additional tables summarize mean and standard deviation for original versus synthetic data to assess distributional consistency.

LanguageEnglish

CategoryResearch & Report

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Pages9

Reading Time23 min

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Supplemental Experimental Results

Feature selection and integration into ensemble ML models

Dataset feature summaries (Tables S1 and S2)

Subject count comparison and statistical summaries (Tables S3–S6)

1 SYNTHETIC DATA ANALYSIS FOR EARLY DETECTION OF ALZHEIMER 2 PROGRESSION THROUGH MACHINE LEARNING ALGORITHMS 3 Ana G. Snchez-Reyna, Ricardo Mendoza-Gonzlez, Huizilopoztli Luna-Garc´ıa, Jos M. 4 Celaya-Padilla, Jorge A. Morgan-Benita, Carlos H. Espino-Salinas, Jorge I. Galvn-Tejada, 5 David Rondon and Klinge O. Villalba-Condori 6 Supplemental Experimental Results 7 As part of the work developed, the following extra results were obtained. 8 These features were processed by RFE and AIC, obtaining diverse sets of features that were then 9 integrated into the ML models within the ensemble (see Table S1 and Table S2) . Experiment Dataset Features | CNvSMC | Orig | “FAQ” | | --- | --- | --- | | (phase 1) | Syn
Custom | “AGE”,“ADAS11”,“ADAS13” and “TRABSCOR”
“AGE” and “ADAS13” | | CNvsEMCI | Orig | “PTGENDER”,“CDRSB” and “FAQ” | | (phase 1) | Syn
Custom | “PTGENDER”,“CDRSB” and “FAQ”
“PTGENDER”,“CDRSB” and “FAQ” | | CNvsLMCI | Orig | “CDRSB”,“LDELTOTAL” and “FAQ” | | (phase 1) | Syn
Custom | “CDRSB”,“LDELTOTAL” and “FAQ”
“CDRSB”,“LDELTOTAL” and “FAQ” | | CNvsAD | Orig | “FAQ” | | (phase 1) | Syn
Custom | “FAQ”
“FAQ” | | SMCvsEMCI | Orig | “AGE”,“CDRSB” and “TRABSCOR” | | (phase 1) | Syn
Custom | “AGE”,“CDRSB” and “TRABSCOR”
“AGE”,“CDRSB” and “TRABSCOR” | | SMCvsLMCI
(phase 1) | Orig
Syn
Custom | “AGE”,“CDRSB”,“ADAS11”,“ADAS13”,“RAVLT.immediate”,“LDELTOTAL” and “TRABSCOR”
“PTGENDER”,“PTMARRY”,“CDRSB”,“ADAS11”,“ADAS13”,“ADASQ4”,“MMSE” and “LDELTOTAL”
“ADAS13” and “LDELTOTAL” | | SMCvsAD
(phase 1) | Orig
Syn
Custom | “CDRSB”,“ADAS13”,“ADASQ4”,“RAVLT.immediate”,“RAVLT.learning”,“LDELTOTAL” and “FAQ”
“CDRSB”,“ADAS13”,“ADASQ4”,“RAVLT.immediate”,“LDELTOTAL” and “FAQ”
“ADAS13” | | EMCIvsLMCI
(phase 1) | Orig
Syn
Custom | “AGE”,“ADAS11”,“ADAS13”,“RAVLT.immediate”,
“LDELTOTAL”,“TRABSCOR” and “FAQ”
“AGE”,“ADAS11”,“ADAS13”,“ADASQ4”,“MMSE”,“RAVLT.immediate”,“LDELTOTAL”,“TRABSCOR” and “FAQ”“AGE”,“ADAS13” and “RAVLT.immediate” | | EMCIvsAD
(phase 1) | Orig
Syn
Custom | “CDRSB”,“ADAS13”,“LDELTOTAL”,“TRABSCOR” and “FAQ”“CDRSB”,“ADAS11”,“ADAS13”,“RAVLT.immediate”,
“RAVLT.learning”,“LDELTOTAL” and “TRABSCOR”
“CDRSB” and “ADAS13” | | LMCIvsAD | Orig | “RAVLT.learning” | | (phase 1) | Syn
Custom | “AGE”,“CDRSB”,“RAVLT.immediate”,“RAVLT.learning” and “FAQ”“AGE” and “CDRSB” | Table S1. Summary of the features of each dataset from phase 1 of the experiments. 10 The Table S3 shows the comparison between the number of subjects in the original data set according 11 to the degree of cognitive impairment with the number of subjects in the synthetic data set. 12 The mean and standard deviation were calculated to analyze the central tendency and statistical 13 dispersion of the data, for the CN vs SMC (phase 1) datasets, the results of these statistical calculations Experiment Dataset Features | CNvsMCI | Orig | “PTGENDER”,“CDRSB” and “ADAS11” | | --- | --- | --- | | (phase 2) | Syn
Custom | “PTGENDER” and “CDRSB”
“CDRSB” | | CNvsAD | Orig | “CDRSB”,“ADASQ4”,“RAVLT.learning” | | (phase 2) | Syn
Custom | “RAVLT.immediate”,“FAQ”
“RAVLT.immediate”,“FAQ” | | MCIvsAD
(phase 2) | Orig
Syn
Custom | “FAQ”
“CDRSB”,“ADAS13”,“RAVLT.immediate”,“RAVLT.learning”,“LDELTOTAL” and “FAQ”
“ADAS13” | Table S2. Summary of the features of each dataset from phase 2 of the experiments. | Dataset | Orig Dataset
No. Subjects | | Syn Dataset
No. Subjects | | | --- | --- | --- | --- | --- | | CNvsSMC (phase 1) | CN | SMC | CN | SMC | | | 85 | 49 | 85 | 85 | | CNvsEMCI (phase 1) | CN | EMCI | CN | EMCI | | | 85 | 88 | 88 | 88 | | CNvsLMCI (phase 1) | CN | LMCI | CN | LMCI | | | 85 | 79 | 85 | 85 | | CNvsAD (phase 1) | CN | AD | CN | AD | | | 85 | 22 | 85 | 85 | | SMCvsEMCI (phase 1) | SMC | EMCI | SMC | EMCI | | | 49 | 88 | 88 | 88 | | SMCvsLMCI (phase 1) | SMC | LMCI | SMC | LMCI | | | 49 | 79 | 79 | 79 | | SMCvsAD (phas

FAQs

What is the purpose of the supplemental experimental results in this document?

To provide extra results supporting the synthetic-data machine learning pipeline for analyzing Alzheimer progression, including selected features, dataset comparisons, and distribution statistics.

How are features selected before being used in machine learning models?

Features are processed by RFE and AIC to produce diverse feature sets, which are then integrated into ensemble ML models.

What do the tables compare between original and synthetic datasets?

They compare the number of subjects across dataset phases and report statistical measures such as mean and standard deviation for multiple features.

Aug 5, 2026

Upload by Liam