Utilizing Machine Learning and employing the XGBoost Classification Technique for evaluating the likelihood of Autism Spectrum Disorder (ASD)

In this study, the XGBoost classification method, a machine learning algorithm investigated, is employed to assess the probability of an individual developing autism spectrum disorder (ASD). The study use a dataset that encompasses a wide range of elements and practical training, such as behavioural characteristics, demographic data, and clinical markers linked to Autism Spectrum Disorder (ASD). The XGBoost method, known for its simplicity and effectiveness in classification tasks, is employed to analyze patterns in the dataset and make predictions related to the likelihood of autism. The research starts with the first stage of data preparation, which access the processes of feature selection and normalisation. These steps are undertaken to optimise the performance of the model. Following this, a thorough assessment of the XGBoost method is undertaken, taking into account various hyperparameter configurations and evaluating its efficacy in properly categorising and accessing people into Autism Spectrum Disorder (ASD) and non-ASD groups. The results demonstrate the effectiveness of the algorithm in identifying subtle patterns within the dataset, so enhancing our comprehensive comprehension of the elements that influence the risk of autism. The implications of the research extend beyond diagnostic applications, as the model's proven accuracy can assist clinicians and researchers in early identification and intervention for individuals at risk of Autism Spectrum Disorder (ASD). This research adds significant value to ongoing efforts focused on enhancing the precision and efficacy of autism risk assessments through the incorporation of machine learning methods. Ultimately, this research aims to provide timely and targeted therapies, leading to enhanced results.