This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on specific anthropometric feature...This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on specific anthropometric features, including age, height, tail length, hair length, bang length, reach, and earlobe type. The dataset was reduced using PCA, which identified height, reach, and age as key features contributing to variance. However, while PCA effectively reduced dimensionality, it faced challenges in clearly distinguishing between the two ethnic groups, a limitation noted in previous research. In contrast, the decision tree model performed significantly better, establishing clear decision boundaries and achieving high classification accuracy. The decision tree consistently selected Height and Reach as the most important classifiers, a finding supported by existing studies on ethnic differences in Northeast India. The results highlight the strengths of combining PCA for dimensionality reduction with decision tree models for classification tasks. While PCA alone was insufficient for optimal class separation, its integration with decision trees improved both the model’s accuracy and interpretability. Future research could explore other machine learning models to enhance classification and examine a broader set of anthropometric features for more comprehensive ethnic group classification.展开更多
Background:Mucopolysaccharidosis(MPS)diseases lead to a profound disruption in normal mechanisms of growth and development.This study was undertaken to determine the general growth of children with MPS I and II.Method...Background:Mucopolysaccharidosis(MPS)diseases lead to a profound disruption in normal mechanisms of growth and development.This study was undertaken to determine the general growth of children with MPS I and II.Methods:The anthropometric data of patients with MPS I and II(n=76)were retrospectively analyzed.The growth patterns of these patients were analyzed and then plotted onto Polish reference charts.Longitudinal analyses were performed to estimate age-related changes.Results:At the time of birth,the body length was greater than reference charts for all MPS groups(Hurler syndrome,P=0.006;attenuated MPS II,P=0.011;severe MPS II,P<0.001).The mean z-score values for every MPS group showed that until the 30th month of life,the growth patterns for all patients were similar.Afterwards,these growth patterns start to differ for individual groups.The body height below the 3rd percentile was achieved around the 30th month for boys with Hurler syndrome,between the 4th and 5th year for patients with severe MPS H and between the 7th and 8th year for patients with attenuated MPS H.Conclusions:The growth pattern differs between patients with MPS I and H.It reflects the clinical severity of MPS and may assist in the evaluation of clinical efficacy of available therapies.展开更多
文摘This study investigates the use of a decision tree classification model, combined with Principal Component Analysis (PCA), to distinguish between Assam and Bhutan ethnic groups based on specific anthropometric features, including age, height, tail length, hair length, bang length, reach, and earlobe type. The dataset was reduced using PCA, which identified height, reach, and age as key features contributing to variance. However, while PCA effectively reduced dimensionality, it faced challenges in clearly distinguishing between the two ethnic groups, a limitation noted in previous research. In contrast, the decision tree model performed significantly better, establishing clear decision boundaries and achieving high classification accuracy. The decision tree consistently selected Height and Reach as the most important classifiers, a finding supported by existing studies on ethnic differences in Northeast India. The results highlight the strengths of combining PCA for dimensionality reduction with decision tree models for classification tasks. While PCA alone was insufficient for optimal class separation, its integration with decision trees improved both the model’s accuracy and interpretability. Future research could explore other machine learning models to enhance classification and examine a broader set of anthropometric features for more comprehensive ethnic group classification.
文摘Background:Mucopolysaccharidosis(MPS)diseases lead to a profound disruption in normal mechanisms of growth and development.This study was undertaken to determine the general growth of children with MPS I and II.Methods:The anthropometric data of patients with MPS I and II(n=76)were retrospectively analyzed.The growth patterns of these patients were analyzed and then plotted onto Polish reference charts.Longitudinal analyses were performed to estimate age-related changes.Results:At the time of birth,the body length was greater than reference charts for all MPS groups(Hurler syndrome,P=0.006;attenuated MPS II,P=0.011;severe MPS II,P<0.001).The mean z-score values for every MPS group showed that until the 30th month of life,the growth patterns for all patients were similar.Afterwards,these growth patterns start to differ for individual groups.The body height below the 3rd percentile was achieved around the 30th month for boys with Hurler syndrome,between the 4th and 5th year for patients with severe MPS H and between the 7th and 8th year for patients with attenuated MPS H.Conclusions:The growth pattern differs between patients with MPS I and H.It reflects the clinical severity of MPS and may assist in the evaluation of clinical efficacy of available therapies.
