C4.5: programs for machine learning
C4.5: programs for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Automated detection of hereditary syndromes using data mining
Computers and Biomedical Research
Principles of data mining
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Automatic Construction of Decision Trees from Data: A Multi-Disciplinary Survey
Data Mining and Knowledge Discovery
Machine Learning
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Predicting breast cancer survivability: a comparison of three data mining methods
Artificial Intelligence in Medicine
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Induction of selective Bayesian classifiers
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
International Journal of Computational Intelligence Studies
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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The prediction of burn patient survivability is a difficult problem to investigate till present times. In present study a prediction Model for patients with burns was built, and its capability to accurately predict the survivability was assessed. We have compared different data mining techniques to asses the performance of various algorithms based on the different measures used in the analysis of information pertaining to medical domain. Obtained results were evaluated for correctness with the help of registered medical practitioners. The dataset was collected from SRT (Swami Ramanand Tirth) Hospital in India, which is one of the Asia's largest rural hospitals. Dataset contains records of 180 patients mainly suffering from burn injuries collected during period from the year 2002 to 2006. Features contain patients' age, sex and percentage of burn received for eight different parts of the body. Prediction models have been developed through rigorous comparative study of important and relevant data mining classification techniques namely, navie bayes, decision tree, support vector machine and back propagation. Performance comparison was also carried out for measuring unbiased estimate of the prediction models using 10-fold cross-validation method. Using the analysis of obtained results, we show that Navie bayes is the best predictor with an accuracy of 97.78% on the holdout samples, further, both the decision tree and support vector machine (SVM) techniques demonstrated an accuracy of 96.12%, and back propagation technique resulted in achieving accuracy of 95%.