Dementia-related user-based collaborative filtering for imputing missing data and generating a reliability scale on clinical test scores

Abstract

Medical doctors may struggle to diagnose dementia, particularly when clinical test scores are missing or incorrect. In case of any doubts, both morphometrics and demographics are crucial when examining dementia in medicine. This study aims to impute and verify clinical test scores with brain MRI analysis and additional demographics, thereby proposing a decision support system that improves diagnosis and prognosis in an easy-to-understand manner. Therefore, we impute the missing clinical test score values by unsupervised dementia-related user-based collaborative filtering to minimize errors. By analyzing succession rates, we propose a reliability scale that can be utilized for the consistency of existing clinical test scores. The complete base of 816 ADNI1-screening samples was processed, and a hybrid set of 603 features was handled. Moreover, the detailed parameters in use, such as the best neighborhood and input features were evaluated for further comparative analysis. Overall, certain collaborative filtering configurations outperformed alternative state-of-the-art imputation techniques. The imputation system and reliability scale based on the proposed methodology are promising for supporting the clinical tests.

Piecewise Weighting Function for Collaborative Filtering Recommendation

“…Collaborative filtering is a main algorithm of e-commerce recommendation system [1]. It can be divided into three types: userbased collaborative filtering [6], item-based collaborative Filtering [7] and model-based collaborative filtering [8]. Next, we will introduce these three methods respectively.…”

Piecewise Weighting Function for Collaborative Filtering Recommendation

“…Collaborative filtering is a main algorithm of e-commerce recommendation system [1]. It can be divided into three types: userbased collaborative filtering [6], item-based collaborative Filtering [7] and model-based collaborative filtering [8]. Next, we will introduce these three methods respectively.…”