Classification Tree Algorithm Research Articles

Predicting the Behavioral Health Needs of Asian Americans in Public Mental Health Treatment: A Classification Tree Approach.

Given the fact that experiencing pandemic-related hardship and racial discrimination worsen Asian Americans' mental health, this study aimed to identify unique characteristics of behavioral health needs among Asian Americans (N = 544) compared to White Americans (N = 78,704) and Black Americans (N = 11,252) who received publicly funded behavioral health services in Indiana before and during the COVID-19 pandemic. We used 2019-2020 Adults Needs and Strengths Assessment (ANSA) data for adults eligible for Medicaid or funding from the state behavioral health agency. Chi-squared automatic interaction detection (CHAID) was used to detect race-specific differences among demographic variables, the pandemic status, and ANSA items. Results indicated that, regardless of age, gender, or pandemic status, Asian Americans who received behavioral health services, struggled more with cultural-related factors compared to White and Black individuals. Within this context, intersections among behavioral/emotional needs (psychosis), life functioning needs (involvement in recovery, residential stability, decision making, medical/physical health), and strengths (job history, interpersonal, and spiritual) further differentiated the mental health functioning of Asian from White and Black Americans. Classification tree algorithms offer a promising approach to detecting complex behavioral health challenges and strengths of populations based on race, ethnicity, or other characteristics.

An Application of the Feature Selection Method Based on Pairwise Correlation for Diagnosis of Ovarian Cancer with Machine Learning

Many machine learning classification problems have high dimensions, and efficient and effective feature selection algorithms are needed to determine the relatively essential features in the dataset. Gene data is often preferred in feature selection applications because it contains many features due to its structure. In addition, it is known from studies in the literature that gene selection plays a significant role in cancer detection. One of the cancer types with very high treatment success in the early period is ovarian cancer. For this purpose, it was aimed to select genes with high descriptiveness in cancer diagnosis by using the ovarian cancer dataset, which is a publicly available dataset. In this study, the feature selection method based on pairwise correlation, which is very new in the literature, was used for classification. Firstly, a feature selection application was made, and 38 genes with the highest cancer descriptors were determined. Then, the classification process was carried out using eight different classification algorithms. After the classification process, the lowest success was for the Extra Tree classification algorithm (with 96.44% accuracy), while the highest was for the Multi-Layer Perceptron, Stochastic Gradient Descent, Logistic Regression, and Support Vector Machine (with 100% accuracy). Although there are many studies on feature selection in the literature, this study is the first application of the current method. In this sense, it is thought that it will contribute to the literature.

A study on predicting the length of hospital stay for Chinese patients with ischemic stroke based on the XGBoost algorithm

BackgroundThe incidence of stroke is a challenge in China, as stroke imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) is an essential indicator of utilization of medical services and is usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on a machine learning algorithm to predict ischemic stroke patients’ LOS.MethodsA total of 18,195 ischemic stroke patients’ electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. The prediction of LOS was regarded as a multi classification problem, and LOS was divided into three categories: 1–7 days, 8–14 days and more than 14 days. After preprocessing the data and feature selection, the XGBoost algorithm was used to build a machine learning model. Ten fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the XGBoost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature importance.ResultsCompared with the naive Bayesian algorithm, logistic region algorithm, decision tree classifier algorithm and ADaBoost classifier algorithm, the XGBoot algorithm has higher ACC, RE and F1 measure. The average ACC, RE and F1 measure were 0.89, 0.89 and 0.89 under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features in terms of hemiplegia aphasia, MRS, NIHSS, TIA, Operation or not, coma index etc. were found to be the top features in importance in predicting the LOS of ischemic stroke patients.ConclusionsThe XGBoost algorithm was an appropriate machine learning method for predicting the LOS of patients with ischemic stroke. Based on the prediction model, an intelligent medical management prediction system could be developed to predict the LOS based on ischemic stroke patients’ electronic medical records.

Machine-learning technique, QSAR and molecular dynamics for hERG–drug interactions

One of the most well-known anti-targets defining medication cardiotoxicity is the voltage-dependent hERG K + channel, which is well-known for its crucial involvement in cardiac action potential repolarization. Torsades de Pointes, QT prolongation, and sudden death are all caused by hERG (the human Ether-à-go-go-Related Gene) inhibition. There is great interest in creating predictive computational (in silico) tools to identify and weed out potential hERG blockers early in the drug discovery process because testing for hERG liability and the traditional experimental screening are complicated, expensive and time-consuming. This study used 2D descriptors of a large curated dataset of 6766 compounds and machine learning approaches to build robust descriptor-based QSAR and predictive classification models for KCNH2 liability. Decision Tree, Random Forest, Logistic Regression, Ada Boosting, kNN, SVM, Naïve Bayes, neural network and stochastic gradient classification classifier algorithms were used to build classification models. If a compound’s IC50 value was between 10 μM and less, it was classified as a blocker (hERG-positive), and if it was more, it was classified as a non-blocker (hERG-negative). Matthew’s correlation coefficient formula and F1score were applied to compare and track the developed models’ performance. Molecular docking and dynamics studies were performed to understand the cardiotoxicity relating to the hERG–gene. The hERG residues interacting after 100 ns are LEU:697, THR:708, PHE:656, HIS:674, HIS:703, TRP:705 and ASN:709 and the hERG–ligand-16 complex trajectory showed stable behaviour with lesser fluctuations in the entire simulation of 200 ns.Communicated by Ramaswamy H. Sarma

Predicting the performance of broadband passive optical networks using machine learning

Abstract Broadband passive optical networks is an established architecture for the high-speed data transfer. For effective fault diagnosis and self-configuration in these networks, analysis of network-generated data is essentially required. In this era, machine learning-based data analytics approaches could play a vital role in analyzing the performance of the networks. In this paper, a machine learning approach has been proposed for predicting the performance of broadband passive optical networks. For this task, a dataset consisting of fiber length, transmission power, number of power splitters, line width, and extinction ratio parameters has been generated to make an estimate of the Q factor for a given optical network. Out of these network parameters, fiber length, transmission power, and several power splitters are selected through the relief attribute evaluation technique. The selected parameters are fed into a regression-based model tree classification algorithm for estimating different levels of Q factor. This work also takes into account logistic regression, decision tree, decision table, PART, and random forest algorithms for the desired task. The analysis of simulation results proves that the regression-based model tree classification algorithm provides an effective estimate of Q factor in terms of accuracy of 93.23% and 95.74% for 7-class and 3-class problems. Thus, this algorithm appears to be a suitable choice to predict the performance of broadband passive optical networks accurately.

Skin cancer detection using effective optical parameters and the classification and regression tree algorithm: A novel framework

Early detection of skin cancer matters because diagnosis, prognosis and treatment plan differ for each skin cancer type at their stages. Medical imaging taking the advantages of the non-invasive and non-ionizing polarized light is emerging as a tool for the development of screening and diagnotic tests. In this work, we proposed a novel framework to classify human melanoma and nonmelanoma skin cancer using the Classification and Regression Tree algorithm (CART). The samples were prepared from twenty-four non-melanoma skin cancer samples (consisting of twelve squamous cell carcinoma and twelve basal cell carcinoma samples); and three melanoma skin cancer samples. We calculated ten optical parameters from anisotropic biological tissues, namely the LB orientation angle (α), the LB phase retardance (β), the CB optical rotation angle (γ), the LD orientation angle (θd), the linear dichroism (D), the circular dichroism (R), the degrees of linear depolarization (e1 and e2), the degree of circular depolarization (e3), and the depolarization index (∆) using Stokes-Mueller matrix formalism. All effective optical parameters of biological tissue were then input into the CART classifier as predictors. The model yielded an accuracy of 92.6%, which is desirable for any robust and interpretable classification model. The results showed that for biological tissue samples, linear polarization properties dominate over circular ones due to the cellular microstructural composition of tissue, especially under anomalous growth as seen in skin cancer. This novel framework can potentially assist physicians in making timely and well-informed medical decisions.

Supply chain risk management of badminton supplies company using decision tree model assisted by fuzzy comprehensive evaluation

AbstractSupply chain risk management is crucial for the management of sports goods companies, which can help enterprises better complete the transparent and efficient management of the whole process from production to sales. However, there are inevitably some potential risks. Thereby, to study the supply chain risk management of badminton supplies companies in the general environment, the supply chain risk evaluation index system of badminton supplies companies is established in this study by studying the connotation of supply chain risk management and combining the steps of fuzzy comprehensive evaluation (FCE) method. Then, according to these evaluation indexes, the modelling process of the classification and regression tree (CART) algorithm in the corporate overall supply chain risk decision tree is proposed. Moreover, the corporate overall supply chain risk decision tree is established. Moreover, the financial data of 10 A‐share listed companies of badminton and other sports goods listed on the Shanghai Stock Exchange in 2020 are selected as the sample dataset for model training and testing. The potential risks of badminton enterprises are predicted. The results show that the scale of badminton enterprises has been increasing gradually since 2018, but the return on total assets has steadily decreased with the increase of years. It shows that the badminton supplies enterprise's input and output assets are unbalanced, and the overall asset operation effect is relatively poor. However, the growth rate of total assets shows a significant upward trend from 2018 to 2020, rising from 4% to 12.69%. It indicates that the corporate investment and domestic and foreign investment expansion in recent years are high‐speed. The FCE method is employed to obtain the maximum eigenvalue of the enterprise, which is 5.4698. Then, the C.I value is 0.1205, the R.I value is 1.12, and the consistency ratio value is 0.0958 < 0.1, indicating that the judgement matrix is acceptable. It suggests that under the influence of the macro environment and economic situation, the sales situation of the badminton supplies company is subtly affected by the macro environment, and there is a great business risk.

Quantitative Analysis of Signal Heterogeneity in the Hepatobiliary Phase of Pretreatment Gadoxetic Acid-Enhanced MRI as a Prognostic Imaging Biomarker in Transarterial Chemoembolization for Intermediate-Stage Hepatocellular Carcinoma.

In the era of local and systemic therapies for intermediate-stage hepatocellular carcinoma (HCC), personalized therapy has become available. The aim of our study was to evaluate the usefulness of quantitative analysis of pretreatment gadoxetic acid-enhanced magnetic resonance imaging (EOB-MRI) to predict prognosis following transarterial chemoembolization (TACE). This retrospective study included patients with treatment-naïve intermediate-stage HCC who underwent EOB-MRI before the initial TACE and were treated by initial TACE between February 2007 and January 2016. Signal heterogeneity in the hepatobiliary phase (HBP) of EOB-MRI was quantitatively evaluated by the coefficient of variation (CV). The cutoff CV value was determined using the Classification and Regression Tree algorithm. A total of 64 patients were enrolled. In multivariate analysis, High CV (≥0.16) was significantly associated with poor prognosis (p = 0.038). In a subgroup analysis of patients within up-to-7 criteria, MST was significantly shorter in the High CV group than in the Low CV group (37.7 vs. 82.9 months, p = 0.024). In patients beyond up-to-7 criteria, MST was 18.0 and 38.3 months in the High CV and Low CV groups, respectively (p = 0.182). In both groups scanned at 1.5 T or 3.0 T, High CV was significantly associated with poor prognosis (p = 0.001 and 0.003, respectively). CV of the tumor in the HBP of EOB-MRI is a valuable prognostic factor of TACE.

Comparing the Efficiency of Heart Disease Prediction using Novel Random Forest, Logistic Regression and Decision Tree And SVM Algorithms

Aim: The aim of the work is to evaluate the accuracy and precision in predicting heart disease using Support Vector Machine (SVM), Random forest (RF), Logistic Regression (LR), Decision Tree (DT) Classification algorithms. Materials and Methods: Classification algorithm is appealed on a heart dataset which consists of 180 records. A framework for heart disease prediction in the medical sector comparing Random forest, Logistic Regression, Decision Tree and SVM classifiers has been proposed and developed. The sample size was calculated as 55 in each group using G power 80%. Sample size was calculated using clincalc analysis, with alpha and beta values 0.05 and 0.5, 95% confidence, pretest power 80% and enrolment ratio 1. Results: The Novel Random Forest Algorithm (92.13%), Support Vector Machine (62.51%), Logistic Regression (84.89%), Decision Tree (86.25%) classifiers produce respectively. SVM, RF exists a statistically significant difference between the two groups (p=0.001,p=.004;p<0.05).LR, RF exists a statistically insignificant difference between the two groups (p=.103, P=.080;p>0.05) both with confidence interval 95%. Hence Random forest is better than SVM, RF, DT classifiers. Conclusion: The results show that the performance of RF is better when compared with SVM, LR and DT in terms of both precision and accuracy.

Supervised study of Novel Random Forest Algorithm for prediction of heart disease in Comparison With The Decision Tree Algorithm

Aim: The aim of this work is to evaluate the accuracy and precision in predicting heart disease using Decision Tree (DT) and Novel Random forest (RF) Classification algorithms. Materials and Methods: Novel Random forest is appealed on a heart dataset which consists of 150 records. A framework for predicting heart disease in the medical field comparing the proposed and developed RF and DT classifiers. Sample Size Calculated as 55 in every group by using 80% G power. Sample Size Calculated using clinical analysis, with Alpha and Beta values of 0.05 and 0.5, the confidence level. confidence is 95%, nicest strength is 80% and registration rate is 1. Results: The Decision Tree classifier produces 96.42% accuracy in predicting the heart disease on the data set, whereas the Random forest classifier predicts the same at the rate of 78.45% of the time with a statistically significant difference between the two groups (p=0.004;p<0.05)with confidence interval 95%. Hence Novel Random forest is better than the Decision Tree. Conclusion: The results show that the performance of Random forest is better compared with Decision Tree in terms of both precision and accuracy.

A Novel Automatic Audiometric System Design Based on Machine Learning Methods Using the Brain's Electrical Activity Signals.

This study uses machine learning to perform the hearing test (audiometry) processes autonomously with EEG signals. Sounds with different amplitudes and wavelengths given to the person tested in standard hearing tests are assigned randomly with the interface designed with MATLAB GUI. The person stated that he heard the random size sounds he listened to with headphones but did not take action if he did not hear them. Simultaneously, EEG (electro-encephalography) signals were followed, and the waves created in the brain by the sounds that the person attended and did not hear were recorded. EEG data generated at the end of the test were pre-processed, and then feature extraction was performed. The heard and unheard information received from the MATLAB interface was combined with the EEG signals, and it was determined which sounds the person heard and which they did not hear. During the waiting period between the sounds given via the interface, no sound was given to the person. Therefore, these times are marked as not heard in EEG signals. In this study, brain signals were measured with Brain Products Vamp 16 EEG device, and then EEG raw data were created using the Brain Vision Recorder program and MATLAB. After the data set was created from the signal data produced by the heard and unheard sounds in the brain, machine learning processes were carried out with the PYTHON programming language. The raw data created with MATLAB was taken with the Python programming language, and after the pre-processing steps were completed, machine learning methods were applied to the classification algorithms. Each raw EEG data has been detected by the Count Vectorizer method. The importance of each EEG signal in all EEG data has been calculated using the TF-IDF (Term Frequency-Inverse Document Frequency) method. The obtained dataset has been classified according to whether people can hear the sound. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms have been applied in the analysis. The algorithms selected in our study were preferred because they showed superior performance in ML and succeeded in analyzing EEG signals. Selected classification algorithms also have features of being used online. Naïve Bayes, Light Gradient Strengthening Machine (LGBM), support vector machine (SVM), decision tree, k-NN, logistic regression, and random forest classifier algorithms were used. In the analysis of EEG signals, Light Gradient Strengthening Machine (LGBM) was obtained as the best method. It was determined that the most successful algorithm in prediction was the prediction of the LGBM classification algorithm, with a success rate of 84%. This study has revealed that hearing tests can also be performed using brain waves detected by an EEG device. Although a completely independent hearing test can be created, an audiologist or doctor may be needed to evaluate the results.

Research on the Intelligent Distribution System of College Dormitory Based on the Decision Tree Classification Algorithm

The trend toward designing an intelligent distribution system based on students’ individual differences and individual needs has taken precedence in view of the traditional dormitory distribution system, which neglects the students’ personality traits, causes dormitory disputes, and affects the students’ quality of life and academic quality. This paper collects freshmen's data according to college students’ personal preferences, conducts a classification comparison, uses the decision tree classification algorithm based on the information gain principle as the core algorithm of dormitory allocation, determines the description rules of students’ personal preferences and decision tree classification preferences, completes the conceptual design of the database of entity relations and data dictionaries, meets students’ personality classification requirements for the dormitory, and lays the foundation for the intelligent dormitory allocation system.

A novel machine learning approach on texture analysis for automatic breast microcalcification diagnosis classification of mammogram images.

Screening programs use mammography as a diagnostic tool for the early detection of breast cancer. Mammogram enhancement is used to increase the local contrast of the mammogram so that the lesions are more visible in the advanced image. For accurate diagnosis in the early stage of breast cancer, the appearance of masses and microcalcification on the mammographic image are two important indicators. The objective of this study was to evaluate the feasibility of the automatic separation of images of breast tissue microcalcifications and also to evaluate its accuracy. The research was carried outby using two techniques of image enhancement and highlighting of breast tissue microcalcifications for the desired areas by regional ROI based on fuzzy system and also Gabor filtering method. After determining the clusters of breast tissue microcalcifications, the clusters are classified using the decision tree classification algorithm. Then, for segmentation, samples suspected of microcalcification are highlighted and masked, and in the last stage, tissue characteristics are extracted. Subsequently, with the help of an artificial neural network (ANN), determining the benign and malignant types of segmented ROI clusters was accomplished. The proposed system is trained with a Digital Database for Screening Mammography (DDSM) developed by the University of South Florida, USA, and the simulations are performed under MATLAB software and the results are compared with previous work. The results of this training performed under this work show an accuracy of 93% and an improvement of sensitivity above 95%. The resultindicates that the proposed approach can be applied to ensure breast cancer diagnosis.

Classification tree algorithm and its application in general archives management system

With the rapid development of higher education in China, more and more archives are managed by the archives of colleges and universities. Therefore, many colleges and universities have equipped archives management software to manage archives by computer. However, at present, the mainstream archives management software for colleges and universities does not have the function of automatic classification of archives. In order to reduce the workload of college archives staff, this paper explores a text automatic classification method suitable for college archives. Classification tree algorithm is a method of organizing and storing information. The classification tree is used to classify data into various categories, such as customers and products. It also provides an effective way to search for data using keywords or phrases. Classification tree is very useful in document processing, because it can help us find documents with similar characteristics, such as customers with similar requirements, products with similar functions, etc. The working principle of classification tree algorithm is to decompose the problem into smaller subproblems, and then solve one subproblem at a time until all problems are completely solved.

The Application of Decision Tree Classification Algorithm on Decision-Making for Upstream Business

The Application of Decision Tree Classification Algorithm on Decision-Making for Upstream Business

Intelligent Fault Diagnosis of Manufacturing Processes Using Extra Tree Classification Algorithm and Feature Selection Strategies

Intelligent Fault Diagnosis of Manufacturing Processes Using Extra Tree Classification Algorithm and Feature Selection Strategies

An evaluation of English distance information teaching quality based on decision tree classification algorithm

An evaluation of English distance information teaching quality based on decision tree classification algorithm

Comparison of the performance of GaussianNB Algorithm, the K Neighbors Classifier Algorithm, the Logistic Regression Algorithm, the Linear Discriminant Analysis Algorithm, and the Decision Tree Classifier Algorithm on same dataset

Abstract: Most educational institutions worldwide have been closed since March 2020 in an effort to slow the spread of the Covid-19 epidemic. More than 90% of students around the world have been influenced by this. In this study, we'll make a prediction about whether or not the Covid-19 epidemic has benefited student performance. Our data will be divided into training and testing datasets, with 80% of the data utilised for training and 20% for testing. To calculate the accuracy of our predictions, we'll use six different algorithms, including the RandomForestClassifier Algorithm, the GaussianNB Algorithm, the K Neighbors Classifier Algorithm, the Logistic Regression Algorithm, the Linear Discriminant Analysis Algorithm, and the DecisionTree Classifier Algorithm.

Sexual Crime Prediction in an African Context

The growth of sexual crime in Africa and the incapacity to control it have had a major physical and psychological impact on victims. Crime in general can reduce foreign direct investment in a country. This study was driven by the need to reduce sexual crime across the country. Data mining techniques were applied to a sexual crime dataset extracted from the South African crime statistics database on Kaggle to visualise sexual crime trends and create a model that predicts the occurrence of sexual crimes, thereby helping government and law enforcement agencies gain insights into the most common sexual crime hotspots across all the nine provinces of South Africa. This model may help law enforcement combat sexual crimes faster. We identified data analytics methods for sexual crime prediction and chose the best one. Because of a linear relationship between the dependent variable (sexual crime) and the independent variables (population and density), linear regression and decision tree classifier algorithms were used to predict main causes of sexual crime in South Africa. Accuracy, precision, recall, and F1 scores were used to test the decision tree algorithm's performance. Linear regression was measured using the R-squared score, which achieved 91% accuracy, indicating how well the model will predict sexual crime occurrences.

Heterogenous ensemble learning driven multi-parametric assessment model for hardware Trojan detection

Semiconductor technologies have gained wide-spread attention across industries due to its use in varied digital signal processing units, healthcare-devices, industrial communication, science and technologies, entertainment, smart devices etc. However, high design-complexities, untrusted foundries, third-party processors etc., have raised the concern of intrusion, particularly Hardware Trojans. Hardware Trojan, a specifically designed micro-intruder that can cause data breaches, system malfunctions etc. And hence may result disastrous consequences. To alleviate such problems, though different methods have been suggested for Trojan detection; however, pre-manufacturing methods can't guarantee Trojan-free design due to non-trusted Foundries and Third-party IPs. Similarly, post-manufacturing methods like side channel analysis or gate -level netlist are limited due to highly complex design, large structure and unavailability of universal golden design structures. Considering it as motivation, this paper proposes a robust register transfer logic (RTL) analysis assisted multi-parameter-based machine learning-driven Hardware Trojan detection system. We applied delay, power consumption and resource utilization profiles to perform Trojan detection over AES-256 circuits. Aforesaid features were extracted using VIVADO tool, which were further employed for resampling process using SMOTE, SMOTE-ENN, SMOTE-Borderline algorithms. Here, the use of resampling intended to alleviate data-imbalance or skewness. Moreover, it helped retaining sufficiently large features for machine learning based training. The resampled features were processed for feature selection using Significant Predictor Test, Cross-Correlation Test and Principal Component Analysis (PCA), distinctly. The selected features were trained using a novel heterogenous ensemble learning model encompassing a total of 11 base-classifiers belonging to Naïve Bayes, Neuro-computing, Decision Tree, Random Forest, Extra Tree classifier and Bagging-AdaBoost algorithms. Applying the maximum voting ensemble, the proposed model performed path-level Trojan detection, where the simulated results affirmed accuracy, F-Measure and Area Under Curve (AUC) of almost 100%, which is higher than any known approaches towards Hardware Trojan detection.