A Hybrid Recommender System using MultiClassifier Regression Model for Autism Detection

Today, there is an requirement of developing rapid medical diagnostic systems which are high in accuracy for the earlier speedy recovery. The observations of the autism spectrum disorder (ASD) are highly rely on the behavioral evaluation of the patients which takes more time and effort. The machine learning models are nowadays used to explore the viability of detecting the features that are probably evaluates the existence of autism or not. The proposed work aimed to address on autism clusters: Adolescent, adult and child dataset using the multi - classifier based regression (MCR) mechanism as a hybrid recommender model to improve the accuracy in prediction. It is constructed by integrating Naive Bayes and Random Forest classifiers with Logistic regression through the meta-classifier with unprocessed data and evaluated by considering the average of probabilities and the majority voting of multiple classifiers. As a whole, with unprocessed data, the MCR model on majority voting performs well on adult and adolescent datasets, whereas no improvement in accuracy for child dataset. To overcome this, enhancement on the MCR model through data preprocessing like selection of behavioral and demographic attributes, data transformation and normalization is experimented. The outcome of the model improved remarkably with majority voting on behavioral analysis for all the three datasets, whereas it suits for adult and child dataset on demographic analysis. The adolescent dataset on demographic attributes accomplished using MCR with average of probabilities. To conclude, based on the overall observations and the interpretations, the new integrated MCR model with processed data performs better than unprocessed data in all datasets and has developed the confidence to be the fittest model in Autism detection.