Autism Spectrum Disorder Prediction Using Machine Learning and Design Science

Journal Title: International Journal of Experimental Research and Review - Year 2024, Vol 39, Issue 3

Abstract

Machine learning, a subset of Artificial Intelligence, has gained much recognition in facilitating disease prediction and the decision-making process in healthcare. One of the most often diagnosed developmental disorders in the world is Autism Spectrum Disorder (ASD). Around the world, it is reported to afflict 75 million people and the number of cases has gradually increased since studies began in the 1960s. The symptoms generally include communication deficits, sensory processing differences, and repetitive actions or behaviors. This research develops a model to detect ASD using Principal Component Analysis and Machine Learning algorithms to classify and predict the risk of ASD among pregnant women. Data was collected from National Hospital in Abuja, Nigeria. According to the results, PCA and Random Forest produced the best accuracy of 98.7%. Bayesian probability was employed to evaluate and verify the reliability of the model. The created model can aid doctors in diagnosing ASD.

Authors and Affiliations

Rajesh Prasad, Farida Musa, Hadiza Muhammad Ahmad, Santosh Kumar Upadhyay,Birendra Kumar Sharma

Keywords

Related Articles

Smart Maintenance of Railway Infrastructure Using Wireless Sensor Networks

The railway infrastructure is a perfect blend of all branches of engineering. Technology has drastically increased, mainly in the Signalling, Civil, Electrical and Mechanical engineering streams. In the field of Signalli...

Exploring the Influence of Arbuscular Mycorrhizal Symbology on the Antioxidant Potential of Liverwort Asterella multiflora: A Comprehensive Study on Rhizoid and Thallus Anatomy

Arbuscular mycorrhizal (AM) symbiosis is a vital ecological interaction between plants and fungi that enhances nutrient uptake and plant resilience. While extensively studied in vascular plants, AM symbiosis in liverwort...

Evaluation of health and nutritional status of adolescent Muslim of North Dum Dum, West Bengal, India

An anthropometric cross-sectional study of urban Bengalee Muslim boys (n=350) aged 11-17 years of North Dum Dum, West Bengal, was undertaken to study their health and nutritional status. The subjects were classified into...

Livelihood Strategies of Street children using the urban space: A case study at Sealdah station area, Kolkata

Social problem are existing in every developing country. In India, too there are several such problems. The problem of street children is one of them. No country or city anywhere in the world today is without the presenc...

Raw fruit juice processing wastewater treatment using electrochemical coagulation followed by synthesis of CuO Nano sorbents using leaf extract

Stainless steel and aluminium electrode was utilized inside batch electro chemicals coagulations (BECCs) using current densities (CD) for the treatment of fruit juice processing wastewater (FJPW). During ECC, ~65-70% col...

Download PDF file
  • EP ID EP736000
  • DOI 10.52756/ijerr.2024.v39spl.017
  • Views 53
  • Downloads 0

How To Cite

Rajesh Prasad, Farida Musa, Hadiza Muhammad Ahmad, Santosh Kumar Upadhyay, Birendra Kumar Sharma (2024). Autism Spectrum Disorder Prediction Using Machine Learning and Design Science. International Journal of Experimental Research and Review, 39(3), -. https://europub.co.uk/articles/-A-736000