Order Table FPMax是基于有序FP-tree结构和二维表的最大频繁模式挖掘算法.有序FP-tree结构可以减少空间的浪费.基于树结构的有序性,算法在挖掘数据时可以减少挖掘事务项的数量,加快挖掘效率.算法采用二维表存储挖据事务项的路径信息及...Order Table FPMax是基于有序FP-tree结构和二维表的最大频繁模式挖掘算法.有序FP-tree结构可以减少空间的浪费.基于树结构的有序性,算法在挖掘数据时可以减少挖掘事务项的数量,加快挖掘效率.算法采用二维表存储挖据事务项的路径信息及交集,采用相应的计算方法可以在不产生条件子树的情况下快速得到最大频繁项集,并避免没必要的挖掘过程减少超集检测,既减少了空间的浪费,又加快了执行效率.展开更多
Objective: We constructed 3D-model of ONFH in computer according to three-dimensional computerized tomography (3D-CT) data. We determined the location and volume of necrosis to investigate its clinical efficacy. Metho...Objective: We constructed 3D-model of ONFH in computer according to three-dimensional computerized tomography (3D-CT) data. We determined the location and volume of necrosis to investigate its clinical efficacy. Method: Totally 92 hips (59 cases) with ONFH (44 males, 15 females) were included, with mean age of 37.5 years (range from 26 to 58). Totally 20 cases (35 hips) were induced by corticosteroid (CTSs), 31 (49 hips) induced by alcohol, 4 (4 hips) induced by trauma and 4 (4 hips) idiopathic. All the hips were categorized into stage ARCO II. Finally diagnosed by MRI, all hips were scanned by CT to acquire data in DICOM format. The images were imported into software to extract 3D-shape of femoral heads, necrotic foci, their volumes and distribution in each quadrant. Deviation of volumes between digital image and biopsy specimen was analyzed by SAS9.1 package. Correlativity between collapse and volume of necrosis under specific pathogeneses was also analyzed. Among the cases necessitating total hip arthroplasty (THA) due to advancing to ARCO III, we randomly selected 8 of them to perform 3D-CT scanning thrice prior to surgical operation. Total femoral heads harvested were torn asunder. Cubic capacity of femoral heads and necrotic foci were hereby measured and compared with those acquired from digital models. Result: Through the digital model, necrotic foci were found mainly locating within the super lateral portion of femoral head, coinciding with those observed in biopsy specimen. Average volumetric ratio of digitally acquired necrosis focus/femoral head in 58 collapsed hips was 36.8%. The ratio of the 34 hips without collapse was 17.3%. In collapsed femoral heads, the distribution of necrosis focus was 23.4% in quadrant 1 (q1), 23.6% in q2, 12.1% in q3, 14.4% in q4, 9.0% in q5, 11.8% in q6, 1.6% in q7 and 3.9% in q8. In femoral heads without collapse, the distribution was 34.2% in q1, 29.6% in q2, 11.8% in q3, 11.3% in q4, 6.0% in q5, 6.0% in q6, 0.5% in q7 and 0.4% in q8. As for the average cubic capacities of femoral heads and necrotic foci, those acquired from the digital model and biopsy specimen had no significant difference in matched-pairs test (t = -1.49, P = 0.179 for femoral heads and t = -1.52, P = 0.172 for necrotic foci). There was significant difference (F = 2.720, P = 0.035 P was respectively 0.0001 and 0.0005). Decision tree model showed that 94.6% (53/56) hips would progress into collapse if the volumetric ratio of necrotic tissue was over 23.48%. Otherwise, if distribution in q2 was over 45.13%, 83.3% (5/6) hips would progress into collapse. No collapse (0/30) would occur if the distribution of necrotic tissue in q2 was under 45.13%. Conclusion: Digital 3D-model reconstructed from CT scanning can precisely incarnate spatial orientation of necrotic foci in femoral head. Multinomial logistic regression and decision-making tree shows that volumetric ratio of necrotic tissues plays an important role in anticipating collapse of femoral head.展开更多
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
文摘Objective: We constructed 3D-model of ONFH in computer according to three-dimensional computerized tomography (3D-CT) data. We determined the location and volume of necrosis to investigate its clinical efficacy. Method: Totally 92 hips (59 cases) with ONFH (44 males, 15 females) were included, with mean age of 37.5 years (range from 26 to 58). Totally 20 cases (35 hips) were induced by corticosteroid (CTSs), 31 (49 hips) induced by alcohol, 4 (4 hips) induced by trauma and 4 (4 hips) idiopathic. All the hips were categorized into stage ARCO II. Finally diagnosed by MRI, all hips were scanned by CT to acquire data in DICOM format. The images were imported into software to extract 3D-shape of femoral heads, necrotic foci, their volumes and distribution in each quadrant. Deviation of volumes between digital image and biopsy specimen was analyzed by SAS9.1 package. Correlativity between collapse and volume of necrosis under specific pathogeneses was also analyzed. Among the cases necessitating total hip arthroplasty (THA) due to advancing to ARCO III, we randomly selected 8 of them to perform 3D-CT scanning thrice prior to surgical operation. Total femoral heads harvested were torn asunder. Cubic capacity of femoral heads and necrotic foci were hereby measured and compared with those acquired from digital models. Result: Through the digital model, necrotic foci were found mainly locating within the super lateral portion of femoral head, coinciding with those observed in biopsy specimen. Average volumetric ratio of digitally acquired necrosis focus/femoral head in 58 collapsed hips was 36.8%. The ratio of the 34 hips without collapse was 17.3%. In collapsed femoral heads, the distribution of necrosis focus was 23.4% in quadrant 1 (q1), 23.6% in q2, 12.1% in q3, 14.4% in q4, 9.0% in q5, 11.8% in q6, 1.6% in q7 and 3.9% in q8. In femoral heads without collapse, the distribution was 34.2% in q1, 29.6% in q2, 11.8% in q3, 11.3% in q4, 6.0% in q5, 6.0% in q6, 0.5% in q7 and 0.4% in q8. As for the average cubic capacities of femoral heads and necrotic foci, those acquired from the digital model and biopsy specimen had no significant difference in matched-pairs test (t = -1.49, P = 0.179 for femoral heads and t = -1.52, P = 0.172 for necrotic foci). There was significant difference (F = 2.720, P = 0.035 P was respectively 0.0001 and 0.0005). Decision tree model showed that 94.6% (53/56) hips would progress into collapse if the volumetric ratio of necrotic tissue was over 23.48%. Otherwise, if distribution in q2 was over 45.13%, 83.3% (5/6) hips would progress into collapse. No collapse (0/30) would occur if the distribution of necrotic tissue in q2 was under 45.13%. Conclusion: Digital 3D-model reconstructed from CT scanning can precisely incarnate spatial orientation of necrotic foci in femoral head. Multinomial logistic regression and decision-making tree shows that volumetric ratio of necrotic tissues plays an important role in anticipating collapse of femoral head.
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.
