Effective Implementation of Pre-Processing Techniques in Machine Learning for Autism Spectrum Disorder
N. Priya1, C. Radhika2
1Dr. N. Priya*, Associate Professor, Department of Computer Science, SDNBV College for Women, Chennai, Tamil Nadu, India.
2C. Radhika, Asst. Professor, Department of Computer Science, SDNBV College for Women, Chennai, Tamil Nadu, India.
Manuscript received on February 10, 2020. | Revised Manuscript received on March 01, 2020. | Manuscript published on March 10, 2020. | PP: 2253-2257 | Volume-9 Issue-5, March 2020. | Retrieval Number: E2676039520/2020©BEIESP | DOI: 10.35940/ijitee.E2676.039520
Open Access | Ethics and Policies | Cite | Mendeley
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Autism Spectrum disorder (ASD) is a neurobiological developmental disorder is symbolize by means of the impairment of social interaction, stereotypic behaviours, and communiqué lack. Early deduction of ASD will enhance the fine of lifestyles of the affected person. The objective of the paper is to focus on the application of various Machine Learning strategies applied for the autism dataset for diagnosing ASD. In this study, the effective pre-processing techniques One-hot encoding, Splitting and Scaling are used to standardize the dataset and the Principal Component Analysis (PCA) evaluator method is applied for the best feature selection. This technique is investigated with various Machine learning techniques like Random Forest, SVM, Logistic Regression, KNN, Naive Bayes. Comparatively, the effective Pre-Processing technique with Random Forest model shows the better accuracy of 92% in diagnosing ASD. When with other metrics such as accuracy, precision, recall, F1-score, ROC, error rate.
Keywords: ASD, Machine Learning Techniques, PCA.
Scope of the Article: Machine Learning