Maxillary sinus floor augmentation using lateral window and crestal technique is considered as predictable methods to increase the residual bone height; however, this surgery is commonly complicated by Schneiderian membrane perforation, which is closely related to anatomical factors. This article aimed to assess anatomical factors on successful augmentation procedures. After review of the current evidence on sinus augmentation techniques, anatomical factors related to the stretching potential of Schneiderian membrane were assessed and a decision tree for the rational choice of surgical approaches was proposed. Schneiderian membrane perforation might occur when local tension exceeds its stretching potential, which is closely related to anatomical variations of the maxillary sinus. Choice of a surgical approach and clinical outcomes are influenced by the stretching potential of Schneiderian membrane. In addition to the residual bone height, clinicians should also consider the stretching potential affected by the membrane health condition, the contours of the maxillary sinus, and the presence of antral septa when evaluating the choice of surgical approaches and clinical outcomes.
Sinus Floor Augmentation ; Decision Trees

In this paper, we focus on classifying cardiac arrhythmias. The MIT-BIH database is used with 14 original classes of labeling which is then mapped into 5 more general classes, using the Association for the Advancement of Medical Instrumentation standard. Three types of features were selected with a focus on the time-frequency aspects of ECG signal. After using the Wigner-Ville distribution the time-frequency plane is split into 9 windows considering the frequency bandwidth and time duration of ECG segments and peaks. The summation over these windows are employed as pseudo-energy features in classification. The “subject-oriented” scheme is used in classification, meaning the train and test sets include samples from different subjects. The subject-oriented method avoids the possible overfitting issues and guaranties the authenticity of the classification. The overall sensitivity and positive predictivity of classification is 99.67 and 98.92%, respectively, which shows a significant improvement over previous studies.
Arrhythmias, Cardiac* ; Classification* ; Decision Trees ; Electrocardiography* ; Methods

The clinical decision analysis (CDA) has used to overcome complexity and uncertainty in medical problems. The CDA is a tool allowing decision-makers to apply evidence-based medicine to make objective clinical decisions when faced with complex situations. The usefulness and limitation including six steps in conducting CDA were reviewed. The application of CDA results should be done under shared decision with patients' value.
Decision Support Techniques* ; Decision Trees* ; Evidence-Based Medicine ; Uncertainty

The purpose of this study was to investigate the use of decision tree for the classification of antimicrobial peptides. The classification was based on the activities of known antimicrobial peptides against common microbes including Escherichia coli and Staphylococcus aureus. A feature selection was employed to select an effective subset of features from available attribute sets.Sequential applications of decision tree with 17 nodes with 9 leaves and 13 nodes with 7 leaves provided the classification rates of 76.74% and 74.66% against E. coli and S. aureus, respectively. Angle subtended by positively charged face and the positive charge commonly gave higher accuracies in both E. coli and S. aureus datasets. In this study, we describe a successful application of decision tree that provides the understanding of the effects of physicochemical characteristics of peptides on bacterial membrane.
Classification* ; Dataset ; Decision Trees* ; Escherichia coli ; Membranes ; Peptides ; Staphylococcus aureus

OBJECTIVE: To provide medicare services for patients demands satisfyingly, immediate introduction of the Customer Relationship Management(CRM) is raised inevitable. In this paper we proposed that the minimizing the hospital losses by cut down the rate of cancelation of the hospital reservation, to secure patients as clients. METHODS: And to implement the data mining-based healthcare customer relationship management system applied from the back propagation algorithm of the artificial neural networks technique and the Feature GENeration(FGEN) algorithm of the decision tree technique. RESULTS: In this paper we divided a patient to an appropriate group through a data mining process and classified more correct customer through a campaign process. CONCLUSION: These results would be essential for new patients to enhance hospital reliability, for hospital to select profitable patients with high loyalty and to manage patients efficiently.
Data Mining* ; Decision Trees ; Delivery of Health Care* ; Humans ; Medicare

With the development of computer technology, medical data has developed from traditional paper pattern into electronic mode, which could effectively promote the medical development. This paper at first presents the status and characteristics of medical data mining. Then, it discusses the critical method of medical data mining in classification, clustering and prediction, respectively. The paper focuses on the application and assessment of five algorithms which are designed for medical data mining, including decision tree, cluster analysis, association rule, intelligent algorithm and the mix algorithm. Finally, this paper outlooks the data mining application in medical domain.
Algorithms ; Cluster Analysis ; Data Mining ; Decision Trees ; Medical Informatics ; Software

BACKGROUND: The best options of several bisphosphonates for prevention of osteoporotic fractures in postmenopausal women remain controversial. We determined which bisphosphonate provides better efficacy in prevention of osteoporotic fractures using a decision analysis tool, in terms of quality of life. METHODS: A decision analysis model was constructed containing final outcome score and the probability of vertebral and hip fracture within 1 year. Final outcome was defined as health-related quality of life, and was used as an utility in the decision tree. Probabilities were obtained by literature review, and health-related quality of life was evaluated by consensus committee. A roll back tool was used to determine the best bisphosphonate, and sensitivity analysis was performed to compensate for decision model uncertainty. RESULTS: The decision model favored bisphosphonate with higher compliance in terms of quality of life. In one-way sensitivity analysis, ibandronate was more beneficial than the others, when probability of compliance on ibandronate was above 0.589. CONCLUSIONS: In terms of quality of life, the decision analysis model showed that compliance was most important for patients in real world, regardless of type of bisphosphonate.
Compliance* ; Consensus ; Decision Support Techniques* ; Decision Trees ; Diphosphonates ; Female ; Hip ; Humans ; Osteoporotic Fractures ; Patient Compliance ; Quality of Life ; Uncertainty

OBJECTIVES: The interpretation of clinical data for the diagnosis of coronary heart disease can be done using algorithms in data mining. Most clinical data interpretation systems for diagnosis developed using data mining algorithms with a black-box approach cannot recognize examination attribute relationships with the incidence of coronary heart disease. METHODS: This study proposes a system to interpretation clinical examination results for the diagnosis of coronary heart disease based the decision tree algorithm. This system comprises several stages. First, oversampling is carried out by a combination of the synthetic minority oversampling technique (SMOTE), feature selection, and the C4.5 classification algorithm. System testing is done using k-fold cross-validation. The performance parameters are sensitivity, specificity, positive prediction value (PPV), negative prediction value (NPV) and the area under the curve (AUC). RESULTS: The results showed that the performance of the system has a sensitivity of 74.7%, a specificity of 93.7%, a PPV of 74.2%, an NPV of 93.7%, and an AUC of 84.2%. CONCLUSIONS: This study demonstrated that, by using C4.5 algorithms, data can be interpreted in the form of a decision tree, to aid the understanding of the clinician. In addition, the proposed system can provide better performance by category.
Area Under Curve ; Classification ; Coronary Disease* ; Data Mining ; Decision Trees ; Diagnosis* ; Heart Diseases ; Incidence ; Sensitivity and Specificity

Data mining differs primarily from traditional data analysis on an important dimension, namely the scale of the data. That is the reason why not only statistical but also computer science principles are needed to extract information from large data sets. In this paper we briefly review data mining, its characteristics, typical data mining algorithms, and potential and ongoing applications of data mining at biopharmaceutical industries. The distinguishing characteristics of data mining lie in its understandability, scalability, its problem driven nature, and its analysis of retrospective or observational data in contrast to experimentally designed data. At a high level one can identify three types of problems for which data mining is useful: description, prediction and search. Brief review of data mining algorithms include decision trees and rules, nonlinear classification methods, memory-based methods, model-based clustering, and graphical dependency models. Application areas covered are discovery compound libraries, clinical trial and disease management data, genomics and proteomics, structural databases for candidate drug compounds, and other applications of pharmaceutical relevance.
Classification ; Data Mining* ; Dataset ; Decision Trees ; Disease Management ; Drug Discovery* ; Genomics ; Proteomics ; Retrospective Studies ; Statistics as Topic

PURPOSE: This study was done to identify protective factors which predict internet game addition in middle school students using data mining decision tree analysis. METHODS: The participants were 557 (Male=233, Female=324) middle school students from G city. Data were collected using structured questionnaires from March, 25 to May, 4, 2013, and analyzed using the descriptive analysis, t-test, ANOVA, decision tree, using SPSS 20.0 program. RESULTS: The result of this research showed the prediction model for protective factors related to internet game addiction. Causative factors included gender, family support and father's attitude as the family protective factor, and planning ability as the personal protective factor. Level of accuracy of the decision tree was 70.6%. CONCLUSION: The different rules classified by the decision tree model in this study should contribute as baseline data for discovering informative knowledge and developing interventions tailored to gender and planning ability in internet game addiction.
Adolescent ; Behavior, Addictive ; Data Mining* ; Decision Trees* ; Humans ; Internet* ; Surveys and Questionnaires