Individualized machine-learning-based clinical assessment recommendation system

Traditional clinical assessments often lack individualization, relying on standardized procedures that may not accommodate the diverse needs of patients, especially in early stages where personalized diagnosis could offer significant benefits. We aim to provide a machine-learning framework that addresses the individualized feature addition problem and enhances diagnostic accuracy for clinical assessments.Individualized Clinical Assessment Recommendation System (iCARE) employs locally weighted logistic regression and Shapley Additive Explanations (SHAP) value analysis to tailor feature selection to individual patient characteristics. Evaluations were conducted on synthetic and real-world datasets, including early-stage diabetes risk prediction and heart failure clinical records from the UCI Machine Learning Repository. We compared the performance of iCARE with a Global approach using statistical analysis on accuracy and area under the ROC curve (AUC) to select the best additional features. The iCARE framework enhances predictive accuracy and AUC metrics when additional features exhibit distinct predictive capabilities, as evidenced by synthetic datasets 1–3 and the early diabetes dataset. Specifically, in synthetic dataset 1, iCARE achieved an accuracy of 0.999 and an AUC of 1.000, outperforming the Global approach with an accuracy of 0.689 and an AUC of 0.639. In the early diabetes and heart disease dataset, iCARE shows improvements of 6–12% in accuracy and AUC across different numbers of initial features over other feature selection methods. Conversely, in synthetic datasets 4–5 and the heart failure dataset, where features lack discernible predictive distinctions, iCARE shows no significant advantage over global approaches on accuracy and AUC metrics. iCARE provides personalized feature recommendations that enhance diagnostic accuracy in scenarios where individualized approaches are critical, improving the precision and effectiveness of medical diagnoses.Author summary: In healthcare, the path to a diagnosis often follows a standard set of procedures. However, this “one-size-fits-all” approach can be inefficient, as the most informative next step for one person might be different for another, especially in the early stages of a disease. To solve this problem, we developed a machine-learning framework called iCARE. Our system works by learning from the health records of past patients to create a personalized model for each new individual. Based on this custom model, it then recommends which specific medical test would be most valuable to collect next to improve diagnostic accuracy. We tested our approach on medical data for conditions like diabetes and heart disease. We found that when different tests are uniquely useful for different types of patients, our personalized system improved diagnostic accuracy by 6–12% over standard methods. Our work demonstrates how machine learning can enhance the dynamic and patient-centered nature of clinical assessments.