Опубликована: Дек. 18, 2024

Recent studies have demonstrated that AI can surpass medical practitioners in diagnostic accuracy, underscoring the increasing importance of AI-assisted diagnosis healthcare. This research introduces SMART-Pred (Shiny Multi-Algorithm R Tool for Predictive Modeling), an innovative AI-based application Alzheimer's disease (AD) prediction utilizing handwriting analysis Our objective is to develop and evaluate a non-invasive, cost-effective, efficient tool early AD detection, addressing need accessible accurate screening methods. methodology employs comprehensive approach AI-driven prediction. We begin with Principal Component Analysis dimensionality reduction, ensuring processing complex data. followed by training evaluation ten diverse, highly optimized models, including logistic regression, Naïve Bayes, random forest, AdaBoost, Support Vector Machine, neural networks. multi-model allows robust comparison different machine learning techniques To rigorously assess model performance, we utilize range metrics sensitivity, specificity, F1-score, ROC-AUC. These provide holistic view each model's predictive capabilities. For validation, leveraged DARWIN dataset, which comprises samples from 174 participants (89 patients 85 healthy controls). balanced dataset ensures fair our models' ability distinguish between individuals based on characteristics. The forest strong achieving accuracy 88.68% test set during analysis. Meanwhile, AdaBoost algorithm exhibited even higher reaching 92.00% after leveraging models identify most significant variables predicting disease. results current clinical tools, typically achieve around 81.00% accuracy. SMART-Pred's performance aligns recent advancements prediction, such as Cambridge scientists' 82.00% identifying progression within three years using cognitive tests MRI scans. Furthermore, revealed consistent pattern across all employed. "air_time" "paper_time" consistently stood out critical predictors (AD). two factors were repeatedly identified influential assessing probability onset, their potential detection risk assessment Even though some limitations exist SMART-Pred, it offers several advantages, being efficient, customizable datasets diagnostics. study demonstrates transformative healthcare, particularly may contribute improved patient outcomes through intervention. Clinical validation necessary confirm whether key this are sufficient accurately real-world settings. step crucial ensure practical applicability reliability these findings practice.

Язык: Английский