Optimizing Data Collection and Management Techniques for Machine Learning Applications in Psychiatry: A Comprehensive Approach to Predicting Autism Spectrum Disorder (ASD) Through Multimodal Data Integration

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ID: 310598
2024
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Abstract
The development of machine learning models for predicting Autism Spectrum Disorder (ASD) requires meticulous data collection and management processes. This paper delves into the systematic approach to acquiring and managing datasets, emphasizing the quality, accuracy, and diversity of the data collected. It discusses the challenges involved in gathering data, addresses ethical considerations for data use, and outlines best practices in managing and securing data. Proper data management strategies ensure the data's integrity and usability, forming the backbone of a robust machine learning model capable of predicting the likelihood of ASD, thus aiding in early diagnosis and effective intervention.
Reference Key
imported_1768936883_696fd5b36b071 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rishi Saxena
Journal International Journal of Science and Social Science Research
Year 2024
DOI
10.5281/zenodo.14048487
URL
https://www.ijsssr.com/j/article/view/264
Keywords Keywords not found

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