Accurate prediction of Alzheimer’s disease (AD) is critical in clinical research and healthcare. However, distinguishing disease progression with heterogeneous clinical symptoms and high-dimensional clinical data across different cognitive stages remains challenging. This study proposes an interpretable machine learning framework for predicting AD using baseline clinical assessments from the National Alzheimer’s Coordinating Center (NACC) cohort.
SHapley Additive exPlanations (SHAP)-guided feature selection was employed to identify the most informative features. Five classifiers were applied across numerical, categorical and their combined subsets in binary and multiclass settings. Notably, the clinically important MCI-to-AD, Random Forest (RF) achieved the best performance, attaining a Balanced Accuracy of 0.9322 and a Macro F1-score of 0.9352.
RF achieved ROC-AUC 0.9755 ± 0.0010 and AUC-PR 0.9924 ± 0.0005, demonstrating the discriminative capability preserved in reduced feature set. These findings demonstrate a promising foundation for future clinical decision-support systems, with integrated interpretable feature selection. Average training times computed across all evaluated classifiers showcased most prominent improvement from 1039.77s to 59.7s (~ 94%).
These findings demonstrate that integrating robust interpretable feature selection with machine learning can provide a promising decision-support framework for future clinical assessment. The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA-funded ADRCs: P30 AG062429 (PI James Brewer, MD, PhD), P30 AG066468 (PI Oscar Lopez, MD), P30 AG062421 (PI Bradley Hyman, MD, PhD), P30 AG066509 (PI Thomas Grabowski, MD), P30 AG066514 (PI Mary Sano, PhD), P30 AG066530 (PI Helena Chui, MD), P30 AG066507 (PI Marilyn Albert, PhD), P30 AG066444 (PI David Holtzman, MD), P30 AG066518 (PI Lisa Silbert, MD, MCR), P30 AG066512 (PI Thomas Wisniewski, MD), P30 AG066462 (PI Scott Small, MD), P30 AG072979 (PI David Wolk, MD), P30 AG072972 (PI Charles DeCarli, MD), P30 AG072976 (PI Andrew Saykin, PsyD), P30 AG072975 (PI Julie A.
Schneider, MD, MS), P30 AG072978 (PI Ann McKee, MD), P30 AG072977 (PI Robert Vassar, PhD), P30 AG066519 (PI Frank LaFerla, PhD), P30 AG062677 (PI Ronald Petersen, MD, PhD), P30 AG079280 (PI Jessica Langbaum, PhD), P30 AG062422 (PI Gil Rabinovici, MD), P30 AG066511 (PI Allan Levey, MD, PhD), P30 AG072946 (PI Linda Van Eldik, PhD), P30 AG062715 (PI Sanjay Asthana, MD, FRCP), P30 AG072973 (PI Russell Swerdlow, MD), P30 AG066506 (PI Glenn Smith, PhD, ABPP), P30 AG066508 (PI Stephen Strittmatter, MD, PhD), P30 AG066515 (PI Victor Henderson, MD, MS), P30 AG072947 (PI Suzanne Craft, PhD), P30 AG072931 (PI Henry Paulson, MD, PhD), P30 AG066546 (PI Sudha Seshadri, MD), P30 AG086401 (PI Erik Roberson, MD, PhD), P30 AG086404 (PI Gary Rosenberg, MD), P20 AG068082 (PI Angela Jefferson, PhD), P30 AG072958 (PI Heather Whitson, MD), P30 AG072959 (PI James Leverenz, MD). School of Computer Sciences, Universiti Sains Malaysia, Penang, 11800, Malaysia Aunsia Khan, Anusha Achuthan & Galib Muhammad Shahriar Himel Metro South Addiction and Mental Health Services, Eight-Mile Plains, 4113, Australia Correspondence to Aunsia Khan or Anusha Achuthan. The authors declare no competing interests.
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