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Data Analysis Machine Learning and Applications Episode 1 Part 7

Tham khảo tài liệu 'data analysis machine learning and applications episode 1 part 7', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 124 Benjamin Georgi M.Anne Spence Pamela Flodman Alexander Schliep 3.2 Phenotype clustering For the phenotype data the NEC model selection indicated two and four component to be good choices with the score for two being slightly better. The clusters for the two component model could readily be identified as a high performance and a low performance cluster with respect to the IQ BD VOC and achievement READING MATH SPELLING features. In fact the diagnosis features did not contribute strongly to the clustering and most were selected to be uninformative in the CSI structure. When considering the four component clustering a more interesting picture arose. The distinctive features of the four clusters can be summarized as 1. high scores IQ and achievement high prevalence of ODD above average general anxiety slight increase in prevalence for many other disorders 2. above average scores high prevalence of transient and chronic tics 3. low performance little comorbidity 4. high performance little comorbidity. Fig. 3. CSI structure matrix for the four component phenotype clustering. Identical colors within each column denote shared use of parameters. Uninformative features are depicted in white. The CSI structure matrix for this clustering is shown in Fig. 3. Identical colors within each column of the matrix denote a shared set of parameters. For instance one can see that cluster 1 has a unique set of parameters for the feature Oppositional Defiancy Disorder ODD and general anxiety GENANX while the other clusters share parameters. This indicates that these two features are distinguishing the cluster from the rest of the data set. The same is true for the transient TIC-TRAN and chronic tics TIC-CHRON features in cluster 2. Moreover one can immediately see that cluster 3 is characterized by distinct parameters for the IQ and achievement features. Finally one can also consider which features are discriminating different clusters. For instance clusters 3 and 4 share parameters .