Applied multivariate techniques
Applied multivariate techniques
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Diagnosis for monitoring system of municipal solid waste incineration plant
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Review: Neural networks and statistical techniques: A review of applications
Expert Systems with Applications: An International Journal
A comparison of neural network methods and Box-Jenkins model in time series analysis
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
A predictive model for cerebrovascular disease using data mining
Expert Systems with Applications: An International Journal
Predicting financial distress of the South Korean manufacturing industries
Expert Systems with Applications: An International Journal
Mining shopping behavior in the Taiwan luxury products market
Expert Systems with Applications: An International Journal
Comparison of regression tree data mining methods for prediction of mortality in head injury
Expert Systems with Applications: An International Journal
Hybrid random forests: advantages of mixed trees in classifying text data
PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Expert Systems with Applications: An International Journal
Customer attrition in retailing: An application of Multivariate Adaptive Regression Splines
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Hypertension is a leading cause of heart disease and stroke. In this study, performance of classification techniques is compared in order to predict the risk of essential hypertension disease. A retrospective analysis was performed in 694 subjects (452 patients and 242 controls). We compared performances of three decision trees, four statistical algorithms, and two neural networks. Predictor variables were age, sex, family history of hypertension, smoking habits, lipoprotein (a), triglyceride, uric acid, total cholesterol, and body mass index (BMI). Classification techniques were grouped using hierarchical cluster analysis (HCA). The data points appeared to cluster in three groups. The first cluster included MLP and RBF. Furthermore CART which was more similar than other techniques linked this cluster. The second cluster included FDA/MARS (degree=1), LR and QUEST, but FDA/MARS (degree=1) and LR was more similar than QUEST. The third cluster included FDA/MARS (degree=2), CHAID and FDA, but FDA/MARS (degree=2) and CHAID was more similar than FDA. MLP and RBF which are one each of neural networks procedures, performed better than other techniques in predicting hypertension. QUEST had a lesser performance than other techniques.