International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 05 | May 2023
p-ISSN: 2395-0072
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Autism Spectrum Disorder Using Machine Learning Mrs. Suhasini, H.N.Chandan2, Manoj Kumar S3, Priyanka K4 1 Professor, Dept. of Computer Science and Engineering, Maharaja Institute of Technology,
Thandavapura
2,3,4,5 Students, Dept, of Computer Science and Engineering, Maharaja Institute of Technology, Thandavapura
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Abstract – Autism Spectrum Disorder(ASD) is a
ASD, ML algorithms may be trained on massive datasets of clinical evaluations, behavioural observations, and other relevant data. ML algorithms, for example, may analyse facial expressions, language use, and other aspects of a person's behaviour to predict the possibility of an ASD diagnosis.
neurodevelopmental illness characterised by difficulties with speech, social interaction, and behaviour. It is a spectrum condition, which means that it affects people differently and that the symptoms vary from person to person. Machine learning is an artificial intelligence technology that enables computer systems to learn and improve their performance without being explicitly programmed. Machine learning may be used to analyse big datasets and uncover patterns that people do not see. Researchers have used machine learning to analyse data from brain scans, behavioural evaluations, and genetic data to better understand the underlying causes of ASD. Machine learning algorithms may also be used to create prediction models that can aid in the earlier and more accurate diagnosis of ASD, as well as to personalise treatment programmes for individuals with ASD. It is crucial to highlight, however, that machine learning is not a cure-all for ASD diagnosis and therapy. Rather, it is a potent tool that can assist researchers and clinicians in better understanding the complexities of ASD and developing more effective interventions. Key Words: Autism Spectrum Symptoms, Machine learning.
Disorder
1.2 Problem Statement Autism Spectrum illness (ASD) is a complicated illness that can be difficult to identify and manage successfully, according to the issue statement. The symptoms and severity of ASD can differ greatly between individuals, making it difficult for healthcare providers to offer reliable diagnosis and design personalised treatment regimens. Furthermore, there is no single test for ASD, and the diagnosis is usually based on a thorough examination of an individual's behaviour, developmental history, and clinical observations. This procedure can be timeconsuming and costly, and more efficient and reliable diagnostic tools are needed.
2. EXISTING SYSTEM
(ASD),
The current approach for diagnosing and managing Autism Spectrum Disorder (ASD) often entails a full review of an individual's behaviour, developmental history, and clinical observations by healthcare experts. This evaluation may involve standardized exams, interviews with carers, and observations of the individual's behaviour. There are also a variety of standardized instruments and exams that are routinely used in the diagnosis of ASD. For example, the Autism Diagnostic Observation Schedule (ADOS) is a standardized evaluation that is extensively used to diagnose ASD. The ADOS is a semi-structured observation of the individual's behaviour that comprises activities and exercises meant to elicit behaviours linked with ASD.
1.INTRODUCTION Autism Spectrum illness (ASD) is a neurodevelopmental illness that impacts social communication and interaction, as well as behaviour and interests. It is often diagnosed in early childhood and is a lifelong disorder, however symptoms and severity can vary greatly across individuals. Individuals with ASD may struggle with social communication, including verbal and nonverbal communication issues, trouble recognising social cues, and difficulty building and sustaining relationships. They may also display repetitive behaviours, habits, and hobbies, in addition to sensory sensitivity.
3. PROPOSED SYSTEM
1.1 Overview
The suggested approach for detecting and managing Autism Spectrum Disorder (ASD) combines machine learning (ML) algorithms with existing clinical evaluation methods. To uncover patterns and traits related with ASD, ML algorithms may be trained on massive datasets of clinical evaluations, behavioural observations, and other relevant data. The use of
Machine learning (ML) is a potent technology for assisting in the diagnosis of Autism Spectrum Disorder (ASD). There is no one test for ASD, and the diagnosis is usually based on a thorough examination of a person's behaviour, developmental history, and clinical observations. To uncover patterns and traits related with
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