Optimizing Parkinson’s Disease Prediction: A Comparative Analysis of Data Aggregation Methods Using Multiple Voice Recordings via an Automated Artificial Intelligence Pipeline

Optimizing Parkinson’s Disease Prediction: A Comparative Analysis of Data Aggregation Methods Using Multiple Voice Recordings via an Automated Artificial Intelligence Pipeline

Patient-level grouped data are prevalent in public health and medical fields, and multiple instance learning (MIL) offers a framework to address the challenges associated with this type of data structure. This study compares four data aggregation methods designed to tackle the grouped structure in c...

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Bibliographic Details
Main Authors: Zhengxiao Yang, Hao Zhou, Sudesh Srivastav, Jeffrey G. Shaffer, Kuukua E. Abraham, Samuel M. Naandam, Samuel Kakraba
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Data
Subjects:
Parkinson’s disease (PD)
machine learning (ML)
artificial intelligence (AI)
multiple instance learning (MIL)
data aggregation
classification
Online Access:https://www.mdpi.com/2306-5729/10/1/4
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