Predicting Autism Spectrum Disorder using Machine Learning Algorithms with Jaundice Symptomatic Analysis
Sharath Chandrika M B1, Nallakaruppan M K2, Siva Rama Krishnan S3, Senthilkumar N C VIT University4
1Sarath Chandrika M B, M.S.(Master of Software), VIT University, Vellore, (Tamil Nadu), India.
2Nallakaruppan M K, School Of Information Technology and Engineering, VIT University, Vellore, (Tamil Nadu), India.
3Siva Rama Krishnan S, School Of Information Technology and Engineering, VIT University, Vellore, (Tamil Nadu), India.
4Senthilkumar N C, School Of Information Technology and Engineering, VIT University, Vellore, (Tamil Nadu), India.
Manuscript received on 23 March 2019 | Revised Manuscript received on 30 March 2019 | Manuscript published on 30 March 2019 | PP: 1011-1014 | Volume-7 Issue-6, March 2019 | Retrieval Number: F2842037619/19©BEIESP
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© 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: We utilized a dataset identified with autism screening all age set of autism: toddler, child, adolescent, adult contained 20 attributes which are used for investigation particularly in deciding persuasive autistic traits, enhancing the order of ASD cases. With 10 social features in addition to 10 individual qualities that have ended up being successful in identifying the ASD cases, consequently applied RT to get the best clusters, process them through RF to get exactness. Primary objective of this work is to predict the correlation between the ASD with its symptoms by applying the machine learning techniques of the data science. The prescribed work is done to predict the correlation between the jaundice symptomatic patients, further progression of the same to ASD. This work also compares the chances of genetic influence which is the secondary classifier that leads to the disorder. To accomplish this objective, we applied our validated supervised Machine Learning, random tree, and random forest.
Keywords: Autism Spectrum Disorder (ASD), Machine Learning (ML), Random Tree (RT), Random Forest (RF), Correctly Classified Instances (CCI), Incorrectly Classified Instances (ICCI), Kappa Statistics(KS), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), Total Number Of Instances (T. INSTANCES), Ignored Class Unknown Instances (ICUI), Aggressive Behaviour (AB), Autism Diagnostic Observation Schedule (ADOS), Autism Genetics Resource Exchange (AGRE), cross – sectional (CS).
Scope of the Article: Predictive Analysis