AI helps predict dementia risk—up to 98% accuracy in tests

Kari Jaaskelainen

13 Jan 2026 — 1 min read

Can AI help flag dementia risk sooner?

A new study applies supervised machine learning to patient health data to predict dementia, including Alzheimer’s disease.

Tested models: K-Nearest Neighbors, Quadratic and Linear Discriminant Analysis, and Gaussian Process classifiers.
Data steps: class balancing with SMOTE and feature vectorization with TF-IDF.
Top result: LDA reached 98% testing accuracy on the study dataset.
Notable signals: presence of the APOE-e4 allele and chronic conditions such as diabetes.
Emphasis on interpretability, with a call for more explainable AI in care settings.

Why it matters: Accurate, transparent models could help clinicians identify people who need follow-up sooner—but these results come from one dataset and need broader validation before clinical use.

Paper: https://arxiv.org/abs/2601.07685v1

Paper: https://arxiv.org/abs/2601.07685v1

Register: https://www.AiFeta.com

#Dementia #Alzheimers #MachineLearning #HealthcareAI #ExplainableAI #APOEe4 #Diabetes #AIResearch

Meet GRACE: a moral governor for safer, more transparent AI

AI agents are getting powerful—so how do we make sure they do the right thing, not just the effective thing? Meet GRACE, a reason-based moral governor that keeps AI behavior aligned with human norms by separating moral reasoning from goal-driven decision-making. * Moral Module: uses deontic logic and explicit reasons

Why some fine-tuned LLMs miss phishing—and how to fix it

Not all fine-tuned LLMs spot phishing equally. A new study tests Llama 3.1 8B, Gemma 2 9B, and Mistral on high-stakes phishing detection—and uses SHAP and mechanistic interpretability to reveal why models do (or don’t) generalize. * Architecture × data diversity matters: Gemma 2 9B hits state-of-the-art performance (F1

AI that explains itself—by following the science

AI that explains itself—by following the science Black-box AI can be powerful, but it often can’t tell us why it made a decision. Concept Bottleneck Models (CBMs) try to fix that by predicting human-understandable concepts first, then the final answer. The catch: standard CBMs ignore domain-specific cause-and-effect and

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A

TL;DR: Finding reliable answers in genomics is hard. GenomAgent turns one big AI into a coordinating team of specialists—and beats the current leader by 12% on a key benchmark. Genomic facts live across many databases. Standard chatbots struggle because they can’t flexibly query those sources. GeneGPT added

Can AI help flag dementia risk sooner?

Read more

Meet GRACE: a moral governor for safer, more transparent AI

Why some fine-tuned LLMs miss phishing—and how to fix it

AI that explains itself—by following the science

Meet GenomAgent: A Team of AI Specialists for Smarter Genomics Q&A