Generation of test oracles using neural network and decision tree model

2014 ◽  
Author(s):  
Vineeta ◽  
Abhishek Singhal ◽  
Abhay Bansal
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Tree Model ◽  
Test Oracles

scholarly journals Index split decision tree and compositional deep neural network for text categorization

2017 ◽  
Vol 7 (1.1) ◽  
pp. 449
Author(s):  
N Ravikumar ◽  
Dr P. Tamil Selvan
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Tree ◽  
Text Categorization ◽  
Deep Neural Network ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Decision Tree Model ◽  
Tree Model ◽  
The Impact

Text categorization with machine learning algorithms generally reckons to possess horizontal set of classes. Several advanced machine learning algorithms have been designed in the past few decades. With the growing research work for text categorization, it has become important to categorize the research outcome and provide the learners with an effective machine learning method, a framework called, Hierarchical Decision Tree and Deep Neural Network (HDT-DNN).It investigates machine learning algorithms to create horizontal set of classes and it is used for classification of text. With this objective, a novel and efficient text categorization framework based on decision tree model is used in order to categorize text according to superior and subordinate level. The text to be categorized is presented in the form of a tree with parent text category being superior to all. The intermediate level represents the text that is both superior and subordinate. Then Deep Neural Network model is presented initiating compositional model, where the text has to be categorized, as a layered integration of primitives from the constructed decision tree model. The extra layers enable composition of features from lower layers, potentially modeling complex text with fewer units than a similarly carried out shallow network producing hierarchical classification. The significance of the impact of HDT-DNN framework is evaluated through empirical study. Extensive experiments are carried out and the performance of HDT-DNN framework is evaluated and compared with existing state-of-art methods using parameters such as precision, classification accuracy, classification time, with respect to varied number of features and document size.

A novel enhanced decision tree model for detecting chronic kidney disease

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Avijit Kumar Chaudhuri ◽  
Deepankar Sinha ◽  
Dilip K. Banerjee ◽  
Anirban Das
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Decision Tree ◽  
Decision Tree Model ◽  
Tree Model

scholarly journals Risk of Pre-Malignancy or Malignancy in Postmenopausal Endometrial Polyps: A CHAID Decision Tree Analysis

Diagnostics ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1094
Author(s):  
Michael Wong ◽  
Nikolaos Thanatsis ◽  
Federica Nardelli ◽  
Tejal Amin ◽  
Davor Jurkovic
Keyword(s):  
Decision Tree ◽  
Expectant Management ◽  
Decision Tree Model ◽  
Decision Tree Analysis ◽  
Focal Lesions ◽  
Tree Model ◽  
Normal Endometrium ◽  
Endometrial Polyps ◽  
Tree Analysis ◽  
Interaction Detection

Background and aims: Postmenopausal endometrial polyps are commonly managed by surgical resection; however, expectant management may be considered for some women due to the presence of medical co-morbidities, failed hysteroscopies or patient’s preference. This study aimed to identify patient characteristics and ultrasound morphological features of polyps that could aid in the prediction of underlying pre-malignancy or malignancy in postmenopausal polyps. Methods: Women with consecutive postmenopausal polyps diagnosed on ultrasound and removed surgically were recruited between October 2015 to October 2018 prospectively. Polyps were defined on ultrasound as focal lesions with a regular outline, surrounded by normal endometrium. On Doppler examination, there was either a single feeder vessel or no detectable vascularity. Polyps were classified histologically as benign (including hyperplasia without atypia), pre-malignant (atypical hyperplasia), or malignant. A Chi-squared automatic interaction detection (CHAID) decision tree analysis was performed with a range of demographic, clinical, and ultrasound variables as independent, and the presence of pre-malignancy or malignancy in polyps as dependent variables. A 10-fold cross-validation method was used to estimate the model’s misclassification risk. Results: There were 240 women included, 181 of whom presented with postmenopausal bleeding. Their median age was 60 (range of 45–94); 18/240 (7.5%) women were diagnosed with pre-malignant or malignant polyps. In our decision tree model, the polyp mean diameter (≤13 mm or >13 mm) on ultrasound was the most important predictor of pre-malignancy or malignancy. If the tree was allowed to grow, the patient’s body mass index (BMI) and cystic/solid appearance of the polyp classified women further into low-risk (≤5%), intermediate-risk (>5%–≤20%), or high-risk (>20%) groups. Conclusions: Our decision tree model may serve as a guide to counsel women on the benefits and risks of surgery for postmenopausal endometrial polyps. It may also assist clinicians in prioritizing women for surgery according to their risk of malignancy.

scholarly journals Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Zhong Xin ◽  
Lin Hua ◽  
Xu-Hong Wang ◽  
Dong Zhao ◽  
Cai-Guo Yu ◽  
...  
Keyword(s):  
Decision Tree ◽  
Predictive Value ◽  
Early Stage ◽  
Current Model ◽  
External Validation ◽  
Area Under The Curve ◽  
Decision Tree Model ◽  
Tree Model ◽  
Chinese Adult ◽  
Significant Difference

We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.

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