A machine learning autism classification based on logistic regression analysis
Fadi Thabtah1, Neda Abdelhamid2, David Peebles3
11Digital Technologies, Manukau Institute of Technology, Auckland, New Zealand.
Insights
Early detection of Autistic Spectrum Disorder (ASD) is crucial. This study introduces a machine learning framework for efficient ASD screening in adults and adolescents, improving diagnostic accessibility.
Area of Science:
- Neurodevelopmental Disorders
- Machine Learning Applications
- Biomedical Data Analysis
Background:
- Autistic Spectrum Disorder (ASD) presents significant healthcare costs, with early diagnosis offering potential cost reductions.
- Effective and accessible screening methods are urgently needed due to the economic impact of autism.
- Limited availability of non-genetic autism screening datasets hinders timely diagnosis.
Purpose of the Study:
- To propose a novel machine learning framework for autism screening in adults and adolescents.
- To identify influential features for autism screening through in-depth data analysis.
- To develop a time-efficient screening tool to aid health professionals and individuals.
Main Methods:
- Development of a machine learning framework for autism screening.
- Utilizing logistic regression for predictive analysis on autism datasets.
- Employing Information Gain (IG) and Chi-square (CHI) testing for feature analysis.
Main Results:
- The machine learning framework demonstrated acceptable classification performance.
- Key features influential in autism screening were identified.
- The proposed methods provide valuable insights for autism screening.
Conclusions:
- Machine learning offers a viable approach for developing effective autism screening tools.
- Feature analysis is critical for optimizing screening tool performance.
- This framework can enhance early identification and diagnosis of ASD.
Abstract:
Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition associated with significant healthcare costs; early diagnosis could substantially reduce these. The economic impact of autism reveals an urgent need for the development of easily implemented and effective screening methods. Therefore, time-efficient ASD screening is imperative to help health professionals and to inform individuals whether they should pursue formal clinical diagnosis. Presently, very limited autism datasets associated with screening are available and most of them are genetic in nature. We propose new machine learning framework related to autism screening of adults and adolescents that contain vital features and perform predictive analysis using logistic regression to reveal important information related to autism screening. We also perform an in-depth feature analysis on the two datasets using information gain (IG) and Chi square testing (CHI) to determine the influential features that can be utilized in screening for autism. Results obtained reveal that machine learning technology was able to generate classification systems that have acceptable performance in terms of sensitivity, specificity and accuracy among others.
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