An enriched intuitionistic kernel based K-medoids clustering for indeterminacy handling in ADHD prediction

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In recent year it is revealed that prevalence of attention-deficit/hyperactivity disorder (ADHD) among primary school children’s is widespread. ADHD is considered as one of the most common childhood disorders and can endure through adolescence and adulthood. Addressing and accurate diagnosis of ADHD in earlier stages will be very effective for proper and timely treatment. But it is very complex to differentiate behaviour that reflect ADHD victim from the normal growth. Though there are several existing works are available for detecting ADHD using machine learning handling indeterminacy is a toughest challenge among researchers. This paper aims at developing an unsupervised learning model-based feature subset selection to eradicate the problem of indeterminacy in handling ADHD prediction. This work adapted introduced the concept of intuitionistic kernel-based k-medoids clustering (IKKMC) for grouping similar type of ADHD patients through the knowledge of degree of membership and degree of hesitation. In this work the outliers are easily handled with intuitionistic fuzzy logic. After performing clustering, the potential feature subset involved in ADHD prediction is identified by applying Recursive Feature elimination model. The simulation results provide the evidence for IKKMC with RFE selected feature subset increases the prediction process of ADHD more accurately than other state of art.




Lalithambigai, M., & Hema, A. (2019). An enriched intuitionistic kernel based K-medoids clustering for indeterminacy handling in ADHD prediction. International Journal of Recent Technology and Engineering, 8(3), 2815–2820.

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