Establishing a Machine Learning-Based Random Forest Classifier to Estimate Autism Risk

The Random Forest model had strong performance in capturing complex patterns within the dataset, with high levels of accuracy, sensitivity, and specificity during both the training and validation stages. The examination of feature significance has shown significant variables that contribute to the predictive capability of the model, hence providing insights into possible biomarkers and risk factors linked to autism. The classifier that has been built shows potential in enabling the implementation of early identification and intervention techniques for those who are at risk of Autism Spectrum Disorder (ASD). Furthermore, the inherent interpretability of the model enables doctors to get valuable insights on the underlying causes that influence its predictions. Future research endeavours include the augmentation of the dataset in order to bolster the model's ability to generalise, as well as the investigation of including nascent biomarkers to further boost the precision and practicality of the classifier in real-life scenarios. In general, the suggested technique based on Random Forest represents a notable advancement in using machine learning for the purpose of personalised risk assessment in autism. This contributes to enhancing the accuracy of diagnosis and the development of customised intervention strategies. This research paper outlines the process of creating and verifying a Random Forest Classifier using machine learning techniques to assess the likelihood of autism. The diagnosis of Autism Spectrum Disorder (ASD) is a challenge due to its complicated nature and the wide range of phenotypic expressions it might present. Using an extensive dataset that includes clinical, genetic, and demographic information, we used a Random Forest algorithm to develop a prediction model for evaluating the likelihood of autism.