An Interpretable Machine Learning Approach for Early Screening of Autism Spectrum Disorder

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@article{1722590,
      author = {Tejeswaran  L},
      title = {An Interpretable Machine Learning Approach for Early Screening of Autism Spectrum Disorder},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {3050-3060},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722590.pdf},
      abstract = {A different way of seeing things marks autism spectrum disorder - trouble connecting socially, challenges in sharing thoughts, repeated actions. Spotting it early changes what happens next; help that comes fast lifts thinking skills, behavior, daily living for kids. Most diagnosis today leans on doctors watching behaviors, their trained opinions - that path? Open to bias, slow, sometimes misses signs until later. Tests run under real conditions show the new method nails accurate forecasts while pulling out the most telling clues behind an autism signal. Because it offers clear results that work at scale, the tool helps doctors make better choices when diagnosing patients. In total, these findings show how algorithms might boost early identification of autism, leading to faster support and improved daily living for those affected by ASD.},
      keywords = {Autism spectrum, Early detection mechanisms, Predictive Modeling, Supervised Learning, Data Analysis},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722590}
  }