Neural-Based Approaches for Improving the Accuracy of Decision Trees

Heart disease is one of the leading cause deaths of worldwide. Prediction of cardiovascular infection is a critical challenge in the area of clinical data analysis. Data mining techniques are providing an effective decision and significant results on data that are used widely for predicting. The purpose of this paper is to propose a novel approach with aims to find a noteworthy method to diagnose heart disease prediction. In this research, a unique dataset was created by combining the Cleveland dataset and Stalog heart disease datasets collected from the UCI ML repository. The new dataset contains 14 medical parameters such as age, sex, blood pressure, and 568 instances for training and prediction heart disease. This paper offers a novel methodology of NNDT (Neural Network and Decision Tree) that uses Neural Network for training model and Decision Tree to test classification for better heart disease prediction. The performance of the proposed approach have been compared with Naive Bayes, Support Vector Machine, Neural Network, Voted Perceptron, and Decision Tree algorithms. The results showed that the accuracy and performance improved as compared to other techniques and methods. This study enables the researchers to analyze the heart disease data with a new approach to predict heart diseases to maintain human health.

At present, patients whose have suffered from stroke in Thailand are increasing every year. Stroke impairments relate to many functions such as sensory, motor function, communication, visual and emotional function which depend on brain's lesion. Physical examinations and assessments are important for planning the rehabilitation programs. For this reason, there are several information for medical decision making. Missing some data for treatment planning may occur. To solve this problem, the proposed study used two algorithms to determine the proper rehabilitation treatment program. Artificial Neural Networks and Decision Trees models were considered. Sensitivity, specificity and accuracy values were computed to define the performance of both algorithms. The results of this study indicated that both techniques can apply for data classification and define the proper treatment programs. However, the results were shown that the specificity and accuracy of decision trees model were higher than neural network model.

For a university student, the main measure of success is whether or not they pass the modules for which they are enrolled. It is not always easy for a lecturer to determine whether or not a student will be successful as there may be various factors on which the final result of a student depends. Applying rules or common insights might aid a lecturer in identifying students who are struggling in a specific module. This may afford the lecturer the opportunity to devise an intervention; so as to enhance the chances of the student passing the module. To facilitate the gathering of information on which to base possible student academic interventions, this study makes use of an artificial neural network and decision tree to derive a series of insights and rules which a lecturer may use to identify struggling students. The artificial neural network and decision tree are both trained using supervised learning techniques. Individually, both the neural network and decision tree have an accuracy in excess of 77% and a sensitivity in excess of 83% on a test data set. Both models show a moderate level of agreement with a pre-labelled dataset.

Development of predictive model for post-stroke depression at discharge based on decision tree algorithm: A multi-center hospital-based cohort study

충수돌기염 환자의 LoS(Length of Stay)를 예측하는 것은 병상의 운영에 적지 않은 영향을 준다. 본 논문에서는 Neural Networks와 Decision Tree를 이용하여 LoS와 연관이 높은 입력변수들을 찾아 그 의미를 분석하며, 찾아낸 입력변수들을 이용하여 다양한 LoS 예측 모형을 개발하고 그 성능을 비교하였다. 모형의 예측 정확성을 높이기 위하여 Bagging과 Boosting 등의 Ensemble 기법도 적용하였다. 실험 결과, Decision Tree 모형이 Neural Networks 모형보다 좀 더 적은 수의 속성을 가지고도 거의 통일한 예측력을 보였으며, Ensemble 기법 중에서는 Bagging 기법이 Boosting 기법보다 좋은 결과를 보여주었다. 의사결정나무 기법은 Neural Networks 기법에 비해 설명력이 있으며, 충수돌기염의 LoS 예측에 매우 효과적이었고, 중요 입력 변수의 선정에도 좋은 결과를 보여줌에 따라 향후 적극적인 기법의 도입이 필요하다고 할 수 있다. For the efficient management of hospital sickbeds, it is important to predict the length of stay (LoS) of appendicitis patients. This study analyzed the patient data to find factors that show high positive correlation with LoS, build LoS prediction models using neural network and decision tree models, and compare their performance. In order to increase the prediction accuracy, we applied the ensemble techniques such as bagging and boosting. Experimental results show that decision tree model which was built with less number of variables shows prediction accuracy almost equal to that of neural network model, and that bagging is better than boosting. In conclusion, since the decision tree model which provides better explanation than neural network model can well predict the LoS of appendicitis patients and can also be used to select the input variables, it is recommended that hospitals make use of the decision tree techniques more actively.

Damage prediction for regular reinforced concrete buildings using the decision tree algorithm

In this paper is presented an experimental study with Data Mining (DM) tools, such as, CMSR Data Miner, KnowledgeSTUDIO and RapidMiner. It is goal to discover at that problems (classification, regression, clustering, association, among others) may be used, including, to verify if can treat Big Data, these tools were applied to three databases: Breast Cancer, Iris and Wine (obtained from the Machine Learning repository from University of California). In the experiments with the CMSR Data Miner tool, the Decision Tree and SOM (Self Organizing Maps) Neural Network algorithms of Data Mining were used. After that, with the KnowledgeSTUDIO tool, Logistic Regression, Deep Learning and K-Means algorithms were used. And finally, with the RapidMiner tool, Decision Tree, Naive Bayes and Deep Learning algorithms of Data Mining were applied. Additionally, it is also with this research aim to decision making by organizational managers, helping them to choose tools from DM more appropriate to their daily work.

This chapter introduces a novel paradigm to impute missing data that combines a decision tree, autoassociative neural network (AANN) model and a principal component analysis-neural network (PCA-NN) based model. These models are designed to answer the crucial question of whether the optimization bounds actually matter. For each model, the decision tree is used to predict search bounds for a hybrid simulated annealing and genetic algorithm method that minimizes an error function derived from the respective model. The models’ ability to impute missing data is tested and then compared using HIV sero-prevalance data. Results indicate an average increase in accuracy of 13% with the AANN based model’s average accuracy increasing from 75.8% to 86.3% while that of the PCA-NN based model increasing from 66.1% to 81.6%.

In this paper we enhance the notion of anomaly detection and use both neural network (NN) and decision tree (DT) for intrusion detection. While DTs are highly successful in detecting known attacks, NNs are more interesting to detect new attacks. In our method we proposed a new approach to design the system using both DT and combination of unsupervised and supervised NN for Intrusion Detection System (IDS). By applying DT known attacks would be recognized with a quick execution time. Unknown attacks would be detected by applying the unsupervised NN based on hybrid of Self Organizing Map (SOM) for clustering attacks into smaller categories and supervised NN based on Backpropagation for detailed clustering.

This work presents a new technique for the identification of multiple power quality disturbances in industrial power systems. This new technique is developed based on change detection filter (CDF) and machine intelligence techniques like the local linear radial basis function neural network (LLRBFNN) and decision tree (DT). The proposed change detection filter has been designed in one cycle back fashion for the multiple non–stationary transient power signal disturbances for localisation, and feature extraction. The extracted features are fed as inputs to either a LLRBFNN or a decision tree–based classifier (DTC) for characterisation of the power quality events. The performance of LLRBFNN and DTC are evaluated and compared with local linear wavelet neural network (LLWNN), wavelet neural network (WNN), radial basis function neural network (RBFNN), etc., to highlight the superior performance of the proposed classifiers.

The leading treads towards social networking services had drawn massive public attention from last one and half decade. The amount of data that is uploaded to these social networking services is increasing day by day. So, their is massive requirement to study the highly dynamic behavior of users towards these services. This is a preliminary work to model the user patterns and to study the effectiveness of machine learning predictive modeling approaches on leading social networking service Facebook. We modeled the user comment patters, over the posts on Facebook Pages and predicted that how many comments a post is expected to receive in next H hrs. To automate the process, we developed a software prototype consisting of the crawler, Information extractor, information processor and knowledge discovery module. We used Neural Networks and Decision Trees, predictive modeling techniques on different data-set variants and evaluated them under Hits@10(custom measure), Area Under Curve, Evaluation Time and Mean Absolute error evaluation metrics. We concluded that the Decision trees performed better than the Neural Networks under light of all evaluation metrics.

Code smells are generally not considered as bugs; instead, they point out certain shortcomings in the software design or code. Identification of code smell is a necessary step for improving the software quality and reducing the maintenance effort. In this study, we introduce a bad smell prediction technique based on object-oriented software metrics that use Decision Tree (DT) and Random Forest (RF) machine learning algorithm. An open-source project, namely JHOTDRAW, was used as our dataset, for which values of object-oriented software metrics were calculated. Two feature selection methods-Random Forest Importance (RFI) and Information Gain (IG) were applied to extract the most relevant attributes for the prediction of code smells, namely, Feature envy, Dispersed coupling, refused parent bequest, and God class. The random-search algorithm was used to tune the parameters of Random Forest and Decision Tree. Results show that the best classification accuracy for Decision Tree was obtained at 99.13% for refused parent bequest code smells. Results also show that after using the Random Forest classifier, refused parent bequest smell was predicted with the best accuracy of 99.14%. Finally, in this research study, a set of code smell prediction rules were extracted using Decision Tree.

Decision trees belong to the most effective classification methods. The main advantage of decision trees is a simple and user-friendly interpretation of the results obtained. But despite its well-known advantages the method has some disadvantages as well. One of them is that decision tree learning algorithm build an “almost optimal” tree. The paper considers the way to improve the efficiency of decision trees. The paper proposes a modification of decision tree learning algorithms by retraining the part of tree at every node training. The classification problems were solved to compare standard decision tree learning algorithms with the modified ones. Algorithm efficiency refers to the percentage of correctly classified test sample objects. Statistical analysis based on Student’s t-test was carried out to compare the efficiency of the algorithms.

This paper takes one thousand records of some medical information system for example, makes data mining according to patients' data record about drug selection, uses BP neural network and decision tree method respectively to have model building and finds the best medicine fits for this kind of disease. The results of the experiment validate the method's feasibility and validity and provide reference frame to assist doctor to use medicine.

Dengue fever is widespread disease in the tropical areas caused by bite of female Eddie mosquito. Pakistan has been victim of this rapidly growing disease since last few years. The world health organization identified the four types of dengue fever. The experts are facing the problem of misdiagnosis of dengue fever. The Tests needed for empirical Classification of Dengue Fever takes a lot of time and money especiallyin epidemic situation in a country with limited resources. Therefore, we have used data mining techniques for the efficient classification of the dengue fever Type. The decision tree learning algorithm has been used as a classification Model. We performed two experiments using decision tree. The first general experiment shows the accuracy of 99.44%. It prunes the attributes which classify the dengue fever on the basis of the values in the dataset. The Second experiment classifies the dengue fever on the basis of expert weighted attributes, which are used in the classification on the basis of Minimum Cost and resource availability. The accuracy of this Model is still high 98.62%. We compared the performance in term of Type II error. We found that the Type II error is very low in second experiment