Background: Diabetes mellitus has several complications. The Late diagnosis of diabetes in people leads to the spread of complications. Therefore, this study has been done to determine the possibility of predicting diabetes type 2 by using data mining techniques. Methods: This is a descriptive-analytic study that was conducted as a cross-sectional study. The study population included people re...

Machine learning approaches based on decision trees (DTs) have been proposed for classifying networking traffic. Although this technique has been proven to have the ability to classify encrypted and unknown traffic, the software implementation of DT cannot cope with the current speed of packet traffic. In this paper, hardware architecture of decision tree is proposed on NetFPGA platform. The pr...

Prosody structure prediction plays an important role in text-tospeech (TTS) conversion systems. It is the must and prior step to parametric prosody prediction. Dynamic programming (DP) and decision tree (DT) are widely used for prosody structure prediction [1][2][3] but with well-known limitations. In this paper, two other new methods, combination of dynamic programming with decision tree and c...

In this paper we experimentally compare the classification uncertainty of the randomised Decision Tree (DT) ensemble technique and the Bayesian DT technique with a restarting strategy on a synthetic dataset as well as on some datasets commonly used in the machine learning community. For quantitative evaluation of classification uncertainty, we use an Uncertainty Envelope dealing with the class ...

Although artificial neural networks can represent a variety of complex systems with a high degree of accuracy, these connectionist models are difficult to interpret. This significantly limits the applicability of neural networks in practice, especially where a premium is placed on the comprehensibility or reliability of systems. A novel artificial neural-network decision tree algorithm (ANN-DT)...

Most existing algorithms for learning Markov network structure either are limited to learning interactions among few variables or are very slow, due to the large space of possible structures. In this paper, we propose three new methods for using decision trees to learn Markov network structures. The advantage of using decision trees is that they are very fast to learn and can represent complex ...

Background: Multiple Sclerosis (MS) is one of the most debilitating disease among young adults. Understanding the disability score (Expanded Disability Status Scale (EDSS)) of these patients is helpful in choosing their treatment process. Calculating EDSS takes a lot of time for Neurologists, so having a way to estimate EDSS can be helpful. This study aimed to estimate the EDSS score of MS pati...

Trypsin is the workhorse protease in mass spectrometry-based proteomics experiments and is used to digest proteins into more readily analyzable peptides. To identify these peptides after mass spectrometric analysis, the actual digestion has to be mimicked as faithfully as possible in silico. In this paper we introduce CP-DT (Cleavage Prediction with Decision Trees), an algorithm based on a deci...

We analyzed the hepatitis data by Decision Tree GraphBased Induction (DT-GBI), which constructs a decision tree for graphstructured data while simultaneously constructing attributes for classification. An attribute at each node in the decision tree is a discriminative pattern (subgraph) in the input graph, and extracted by Graph-Based Induction (GBI). We conducted four kinds of experiments usin...

The target of the task is to foresee coronary illness by Novel Decision Tree (DT) in examination with k-Nearest Neighbor (KNN) utilizing Cleveland dataset. Coronary Disease forecasting performed applying (N=20) and algorithms. algorithm uses tree structure make decisions. K-nearest neighbor an easy approach solve regression classification problems. heart dataset utilized for identification pred...