A machine learning based approach to classify autism with optimum behavior sets

Machine Learning based behavioural analytics emphasis the need to develop accurate prediction models for detecting the risk of autism faster than the traditional diagnostic methods. Quality of prediction rely on the accuracy of the supplied dataset and the machine learning model.To improve accuracy of prediction, dimensionality reduction with feature selection is applied to eliminate noisy features from a dataset. In this work an ASD diagnosis dataset with 21 features obtained from UCI machine learning repository is experimented with swarm intelligence based binay firefly feature selection wrapper. The alternative hypothesis of the experiment claims that it is possible for a machine learning model to achieve a better classification accuracy with minimum feature subsets.Using Swarm intelligence based single-objective binary firefly feature selection wrapper it is found that 10 features among 21 features of ASD dataset are sufficient to distinguish between ASD and non-ASD patients.The results obtained with our approach justifies the hypothesis by producing an average accuracy in the range of 92.12%-97.95% with optimum feature subsets which is approximately equal to the average accuracy produced by entire ASD diagnosis dataset.