Background and Objectives: Autism is the most well-known disease that occurs in any age people. There is an increasing concern in appealing machine learning techniques to diagnose these incurable conditions. But, the poor quality of most datasets contains the production of efficient models for the forecast of autism. The lack of suitable pre-processing methods outlines inaccurate and unstable results. For diagnosing the disease, the techniques handled to improve the classification performance yielded better results, and other computerized technologies were applied.
Methods: An effective and high performance model was introduced to address pre-processing problems such as missing values and outliers. Several based classifiers applied on a well-known autism data set in the classification stage. Among many alternatives, we remarked that combine replacement with the mean and improvement selection with Random Forest and Decision Tree technologies provide our obtained highest results.
Results: The best-obtained accuracy, precision, recall, and F-Measure values of the MVO-Autism suggested model were the same, and equal 100% outperforms their counterparts.
Conclusion: The obtained results reveal that the suggested model can increase classification performance in terms of evaluation metrics. The results are evidence that the MVO-Autism model outperforms its counterparts. The reason is that this model overcomes both problems.
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Shahid Rajaee Teacher Training University