Prediction of Declarative Memory Profile in Panic Disorder Patients: a Machine Learning-based Approach

Overview

Journal Braz J Psychiatry

Specialty Psychiatry

Date 2023 Oct 25

PMID 37879064

Authors

Felipe Dalvi-Garcia

Laiana Azevedo Quagliato

Donald J Bearden

Antonio Egidio Nardi

Affiliations

Soon will be listed here.

Abstract

Objective: To develop a classification framework based on random forest (RF) modeling to outline the declarative memory profile of patients with panic disorder (PD) compared to a healthy control sample.

Methods: We developed RF models to classify the declarative memory profile of PD patients in comparison to a healthy control sample using the Rey Auditory Verbal Learning Test (RAVLT). For this study, a total of 299 patients with PD living in the city of Rio de Janeiro (70.9% females, age 39.9 ± 7.3 years old) were recruited through clinician referrals or self/family referrals.

Results: Our RF models successfully predicted declarative memory profiles in patients with PD based on RAVLT scores (lowest area under the curve [AUC] of 0.979, for classification; highest root mean squared percentage [RMSPE] of 17.2%, for regression) using relatively bias-free clinical data, such as sex, age, and body mass index (BMI).

Conclusions: Our findings also suggested that BMI, used as a proxy for diet and exercises habits, plays an important role in declarative memory. Our framework can be extended and used as a prospective tool to classify and examine associations between clinical features and declarative memory in PD patients.

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