Predicting Conversion from SCD to MCI: A Machine Learning Study

Abstract

BACKGROUNDSubjective cognitive decline (SCD) may precede mild cognitive impairment (MCI), but not all individuals with SCD progress to MCI. Identifying which individuals are most likely to convert and over what time frame remains an important goal in Alzheimers disease research. MRI measures of white matter hyperintensity (WMH) burden and gray matter (GM) atrophy may improve prediction beyond demographic and cognitive predictors, but their incremental value across different time intervals has not been established.

METHODSData were obtained from four longitudinal cohorts (ADNI, NACC, CIMA-Q, and PREVENT-AD). A total of 1,352 participants with SCD at baseline were included. Machine learning models (logistic regression, random forest, XGBoost) were used to predict conversion from SCD to MCI at 2 years, 3 years, 4 years, and 5-year horizons. Four feature sets were compared: base (age, sex, education, APOE4, hypertension), base and cognition (adding MoCA and Trail Making Test Part B), base and MRI (adding regional WMH and GM volumes), and combined (all features).

RESULTSThe base and MRI set achieved the highest Area Under the Curve (AUC) at the 3-year (0.900), 4-year (0.879), and 5-year (0.923) horizons. The combined set achieved the highest AUC at the 2-year horizon only (0.884). MRI features produced larger AUC gains over the base model than cognitive features at the 3, 4, 5-year horizons. Parietal WMH was the most frequently selected MRI predictor.

CONCLUSIONSMRI features, particularly regional WMH and GM volumes, provided greater predictive value than cognitive features at longer prediction horizons. A select number of regional MRI features predicted SCD to MCI conversion with high accuracy up to 5 years in advance.

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BibTeX RIS

BibTeX

@misc{kamal2026predicting,
  title = {{Predicting Conversion from SCD to MCI: A Machine Learning Study}},
  author = {F. Kamal and A. Metz and K. Chadwick and R. Moqadam and C. Morrison and M. Dadar},
  howpublished = {medRxiv},
  year = {2026},
  month = sep,
  doi = {10.64898/2026.09.04.26362240},
  url = {https://www.medrxiv.org/content/10.64898/2026.09.04.26362240v1},
  note = {CC BY-NC-ND},
}

RIS

TY  - UNPB
TI  - Predicting Conversion from SCD to MCI: A Machine Learning Study
AU  - F. Kamal
AU  - A. Metz
AU  - K. Chadwick
AU  - R. Moqadam
AU  - C. Morrison
AU  - M. Dadar
PY  - 2026
DA  - 2026/09/04
PB  - medRxiv
DO  - 10.64898/2026.09.04.26362240
UR  - https://www.medrxiv.org/content/10.64898/2026.09.04.26362240v1
AB  - BACKGROUNDSubjective cognitive decline (SCD) may precede mild cognitive impairment (MCI), but not all individuals with SCD progress to MCI. Identifying which individuals are most likely to convert and over what time frame remains an important goal in Alzheimers disease research. MRI measures of white matter hyperintensity (WMH) burden and gray matter (GM) atrophy may improve prediction beyond demographic and cognitive predictors, but their incremental value across different time intervals has not been established.  METHODSData were obtained from four longitudinal cohorts (ADNI, NACC, CIMA-Q, and PREVENT-AD). A total of 1,352 participants with SCD at baseline were included. Machine learning models (logistic regression, random forest, XGBoost) were used to predict conversion from SCD to MCI at 2 years, 3 years, 4 years, and 5-year horizons. Four feature sets were compared: base (age, sex, education, APOE4, hypertension), base and cognition (adding MoCA and Trail Making Test Part B), base and MRI (adding regional WMH and GM volumes), and combined (all features).  RESULTSThe base and MRI set achieved the highest Area Under the Curve (AUC) at the 3-year (0.900), 4-year (0.879), and 5-year (0.923) horizons. The combined set achieved the highest AUC at the 2-year horizon only (0.884). MRI features produced larger AUC gains over the base model than cognitive features at the 3, 4, 5-year horizons. Parietal WMH was the most frequently selected MRI predictor.  CONCLUSIONSMRI features, particularly regional WMH and GM volumes, provided greater predictive value than cognitive features at longer prediction horizons. A select number of regional MRI features predicted SCD to MCI conversion with high accuracy up to 5 years in advance.
ER  -

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