A scalable decision tree system and its application in pattern recognition and intrusion detection

This dissertation contributes with novel predictive modelling approaches to data-driven computational psychiatry and offers alternative analyses frameworks to the standard statistical analyses in psychiatric research. In particular, this document advances research in medical data mining, especially psychiatry, via two phases. In the first phase, this document promotes research by proposing synergistic machine learning and statistical approaches for detecting patterns and developing predictive models in clinical psychiatry data to classify diseases, predict treatment outcomes or improve treatment selections. In particular, these data-driven approaches are built upon several machine learning techniques whose predictive models have been pre-processed, trained, optimised, post-processed and tested in novel computationally intensive frameworks. In the second phase, this document advances research in medical data mining by proposing several novel extensions in the area of data classification by offering a novel decision tree algorithm, which we call PIDT, based on parameterised impurities and statistical pruning approaches toward building more accurate decision trees classifiers and developing new ensemblebased classification methods. In particular, the experimental results show that by building predictive models with the novel PIDT algorithm, these models primarily led to better performance regarding accuracy and tree size than those built with traditional decision trees. The contributions of the proposed dissertation can be summarised as follow. Firstly, several statistical and machine learning algorithms, plus techniques to improve these algorithms, are explored. Secondly, prediction modelling and pattern detection approaches for the first-episode psychosis associated with cannabis use are developed. Thirdly, a new computationally intensive machine learning framework for understanding the link between cannabis use and first-episode psychosis was introduced. Then, complementary and equally sophisticated prediction models for the first-episode psychosis associated with cannabis use were developed using artificial neural networks and deep learning within the proposed novel computationally intensive framework. Lastly, an efficient novel decision tree algorithm (PIDT) based on novel parameterised impurities and statistical pruning approaches is proposed and tested with several medical datasets. These contributions can be used to guide future theory, experiment, and treatment development in medical data mining, especially psychiatry.

Data mining is nontrivial extraction of implicit, previously unknown and potential useful information from the data. For a database with number of records and for a set of classes such that each record belongs to one of the given classes, the problem of classification is to decide the class to which the given record belongs. The classification problem is also to generate a model for each class from given data set. We are going to make use of supervised classification in which we have training dataset of record, and for each record the class to which it belongs is known. There are many approaches to supervised classification. Decision tree is attractive in data mining environment as they represent rules. Rules can readily expressed in natural languages and they can be even mapped o database access languages. Now a days classification based on decision trees is one of the important problems in data mining which has applications in many areas. Now a days database system have become highly distributed, and we are using many paradigms. we consider the problem of inducing decision trees in a large distributed network of highly distributed databases. The classification based on decision tree can be done on the existence of distributed databases in healthcare and in bioinformatics, human computer interaction and by the view that these databases are soon to contain large amounts of data, characterized by its high dimensionality. Current decision tree algorithms would require high communication bandwidth, memory, and they are less efficient and scalability reduces when executed on such large volume of data. So there are some approaches being developed to improve the scalability and even approaches to analyse the data distributed over a network.[keywords: Data mining, Decision tree, decision tree induction, distributed data, classification]

Decision Model for Acute Appendicitis Treatment With Decision Tree Technology—A Modification of the Alvarado Scoring System

Hypertension is a critical public health concern worldwide. Identification of risk factors using traditional multivariable models has been a field of active research. The present study was undertaken to identify risk patterns associated with hypertension incidence using data mining methods in a cohort of Iranian adult population.Data on 6205 participants (44% men) age > 20 years, free from hypertension at baseline with no history of cardiovascular disease, were used to develop a series of prediction models by 3 types of decision tree (DT) algorithms. The performances of all classifiers were evaluated on the testing data set.The Quick Unbiased Efficient Statistical Tree algorithm among men and women and Classification and Regression Tree among the total population had the best performance. The C-statistic and sensitivity for the prediction models were (0.70 and 71%) in men, (0.79 and 71%) in women, and (0.78 and 72%) in total population, respectively. In DT models, systolic blood pressure (SBP), diastolic blood pressure, age, and waist circumference significantly contributed to the risk of incident hypertension in both genders and total population, wrist circumference and 2-h postchallenge plasma glucose among women and fasting plasma glucose among men. In men, the highest hypertension risk was seen in those with SBP > 115 mm Hg and age > 30 years. In women those with SBP > 114 mm Hg and age > 33 years had the highest risk for hypertension. For the total population, higher risk was observed in those with SBP > 114 mm Hg and age > 38 years.Our study emphasizes the utility of DTs for prediction of hypertension and exploring interaction between predictors. DT models used the easily available variables to identify homogeneous subgroups with different risk pattern for the hypertension.

In view of the fact that the existing decision tree models have not considered the decision makers’ preference behaviors during the classification process, these models cannot predict the problems with obvious preference very well. Driven by demand, this paper puts forward a new decision tree algorithm—a preference cost sensitive decision tree (PCSDT). This algorithm introduces the preference degree and preference cost to decision tree. Then, we utilize the effective preference to construct a new attribute selection factor (ASF) and the class label distribution rule for a node. Finally, a preference sensitive decision tree with best preference degree is generated by adaptively adjusting the preference degree. Experiments have proved that the proposed method can guarantee better overall accuracy and performances than other traditional algorithms.

Teicoplanin 24-h loading dose regimen using a decision tree model to target serum trough concentration of 15–30 μg/mL: A retrospective study

Background: Tick-borne encephalitis and Lyme borreliosis are the most common natural focal infections in Russia often arising as a mixed infection, which is often clinically difficult to distinguish from a monoinfection at the onset of the disease that is a result of delayed laboratory verification of the diagnosis and it requires further searching for fundamentally new approaches to the issue of early differential diagnosis of tick-borne infections.Aims: is to develop decision tree algorithms for early differential diagnosis between the mono- and mixed forms of acute Lyme borreliosis and tick-borne encephalitis with prevailing febrile syndrome in clinical picture based on clinical and laboratory data.Materials and methods: We retrospectively analyzed 55 clinical and laboratory parameters obtained from 291 hospitalized tick-borne infections patients with or without erythema migrans at the site of ixodid tick bites in the first week of the disease, who were included in the single-center study from 2010 to 2023. In 211 patients without erythema, the analysis was carried out between three classes depending on the diagnosis: the mixed infection of non-erythematous Lyme borreliosis and tick-borne encephalitis, the mono-infection of non-erythematous Lyme borreliosis or the monoinfection of tick-borne encephalitis. The other two classes, which included 80 patients with erythema, had the mixed infection of acute erythematous Lyme borreliosis and tick-borne encephalitis or erythematous Lyme borreliosis monoinfection. Python programming language was applied to develop two decision tree models. Feature importance was assessed for all predictors. Each patient class was randomly divided into training (70%) and testing (30%) datasets. Accuracy evaluation of the models was based on ROC analysis.Results: The decision tree algorithm for early differential diagnosis among the tick-borne infection patients without erythema migrans included the following most important predictors: maximal fever rise, chills, neutrophil-to-monocyte ratio, ESR, absolute number of reactive lymphocytes and immature granulocytes, and percentage of eosinophils. The model for differential diagnosis between the patients with erythema migrans included the following predictors: maximal fever rise, the absolute number of reactive lymphocytes and immature granulocytes, and the percentage of basophils. Both decision tree models showed excellent predictive values based on sensitivity, specificity, precision, accuracy, and F1 scores, as well as areas under the ROC curve, which were higher than 0.90.Conclusions: Based on clinical and laboratory parameters, two decision tree models with high sensitivity have been developed, which can be easily applied in clinical practice for early differential diagnosis of the tick-borne infections with prevailing fever syndrome.

To develop and test decision tree (DT) models to classify physical activity (PA) intensity from accelerometer output and Gross Motor Function Classification System (GMFCS) classification level in ambulatory youth with cerebral palsy (CP) and compare the classification accuracy of the new DT models to that achieved by previously published cut points for youth with CP. Youth with CP (GMFCS levels I-III) (N = 51) completed seven activity trials with increasing PA intensity while wearing a portable metabolic system and ActiGraph GT3X accelerometers. DT models were used to identify vertical axis (VA) and vector magnitude (VM) count thresholds corresponding to sedentary (SED) (<1.5 METs), light-intensity PA (LPA) (≥1.5 and <3 METs) and moderate-to-vigorous PA (MVPA) (≥3 METs). Models were trained and cross-validated using the "rpart" and "caret" packages within R. For the VA (VA_DT) and VM DT (VM_DT), a single threshold differentiated LPA from SED, whereas the threshold for differentiating MVPA from LPA decreased as the level of impairment increased. The average cross-validation accuracies for the VC_DT were 81.1%, 76.7%, and 82.9% for GMFCS levels I, II, and III. The corresponding cross-validation accuracies for the VM_DT were 80.5%, 75.6%, and 84.2%. Within each GMFCS level, the DT models achieved better PA intensity recognition than previously published cut points. The accuracy differential was greatest among GMFCS level III participants, in whom the previously published cut points misclassified 40% of the MVPA activity trials. The GMFCS-specific cut points provide more accurate assessments of MVPA levels in youth with CP across the full spectrum of ambulatory ability.

This paper proposes an integrated approach utilizing Fuzzy Logic and Decision Tree algorithms to diagnose early-stage faults in power transformers based on Dissolved Gas Analysis (DGA) test results of transformer insulation oil. Overcoming limitations in conventional methods such as Duval Triangle, Key Gas Analysis, Rogers Ratio, IEC Ratio, and Doernenburg Ratio, our Fuzzy Logic and Decision Tree models address issues like inaccurate diagnosis, inconsistent diagnosis, lack of decisions or out-of-code results, and time-intensive manual calculations for large DGA datasets. The Decision Tree algorithm, a machine learning technique is applied to categorize faults into thermal and electrical types. Trained with over 300 DGA samples from transformers with known faults, the models exhibit robust performance during testing with different datasets. Notably, the Duval Triangle decision tree model attains the highest accuracy among the ten developed models, achieving a 98% accuracy rate when tested with 50 samples with known faults. Moreover, Decision Tree models for KGA, Doernenburg, Rogers, and IEC also demonstrate substantial prediction accuracy at 92%, 86%, 92%, and 90% respectively underscoring the efficacy of artificial intelligence methods over traditional approaches.

In order to timely and properly analyse the customer credit assessment, and to speed up the decision-making speed, here introduced the application of data mining technology used in the bank on the users” evaluation, and establish a decision tree model of customer credit evaluation which is aimed at improving the credit rating quality . And introduced the basic process of decision tree algorithm. Bank customer’s credit is classified as background. Using the decision tree algorithm C4.5 as the most classic tools, specific studies of business understanding, data understanding, data preparation, modeling, evaluation and implementation of aspects of publishing have been done. Customer credit, using the decision tree classification algorithm, obtained a series of decision rules with which a bank to make the right decisions.

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

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

Methods are needed for creating models to characterize verbal communication between therapists and their patients that are suitable for teaching purposes without losing analytical potential. A technique meeting these twin requirements is proposed that uses decision trees to identify both change and stuck episodes in therapist-patient communication. Three decision tree algorithms (C4.5, NBTree, and REPTree) are applied to the problem of characterizing verbal responses into change and stuck episodes in the therapeutic process. The data for the problem is derived from a corpus of 8 successful individual therapy sessions with 1760 speaking turns in a psychodynamic context. The decision tree model that performed best was generated by the C4.5 algorithm. It delivered 15 rules characterizing the verbal communication in the two types of episodes. Decision trees are a promising technique for analyzing verbal communication during significant therapy events and have much potential for use in teaching practice on changes in therapeutic communication. The development of pedagogical methods using decision trees can support the transmission of academic knowledge to therapeutic practice.

In order to survive in the modern world, organizations must be equipped with the mechanisms that not only maintain their competitive advantage, but also result in their progress and improvement. Prediction of banks’ performances is an important issue, and a poor performance in banks may primarily lead to their bankruptcy, thereby affecting national economics. The bank performance prediction model uses scientific and systematic approaches to diagnose the financial operations of institutes. According to a precise and strict evaluation, the model can detect the weakness of institutions in advance and provide early warning signals to related financial governments. In the present study, we have used three data mining models to predict the future performance of the banks accepted in Tehran Stock Exchange (TSE) and Iran Fara Bourse. Initially, 53 financial ratios were selected and, consequently, reduced to 28 using the fuzzy Delphi technique. The statistical population included 18 banks listed on TSE and Iran Fara Bourse, which provided their financial statements during the period of 2011 to 2017. Data were collected from the Codal site based on 28 financial ratios using C4.5 decision tree, AdaBoost, and Naive Bayes algorithm. According to the findings, the Naive Bayes algorithm was the optimal predictive model with the accuracy of 88.89%.

--Heart disease is one of the notable causes of death in worldwide everyday divination of heart disease is a critical challenge in area of medical data analysis. Machine learning (ML) has been shown to be successful in helping in making decision and prediction from huge quantity of data produced by medical health care industry. Medical data mining is an important area of data mining and considered as one of the important research field due to the application in medical health care domain. In medical data mining classification and prediction of medical dataset process challenges. It is difficult to medical practice to predict the heart attack as it complex task. The health sector at present contain the information that are hidden and which are important in making decision. Data mining algorithms such as Naive Bayes algorithm, decision tree algorithm and random forest are applied in the research for heart disease prediction the result show the comparison between the three algorithm and selecting the best one among three, the Random forest algorithm will provide the more accuracy can compared to all the three algorithms.