VERONICA: Visual Analytics for Identifying Feature Groups in Disease Classification

The use of data analysis techniques in electronic health records (EHRs) offers great promise in improving predictive risk modeling. Although useful, these analysis techniques often suffer from a lack of interpretability and transparency, especially when the data is high-dimensional. The emergence of a type of computational system known as visual analytics has the potential to address these issues by integrating data analysis techniques with interactive visualizations. This paper introduces a visual analytics system called VERONICA that utilizes the natural classification of features in EHRs to identify the group of features with the strongest predictive power. VERONICA incorporates a representative set of supervised machine learning techniques—namely, classification and regression tree, C5.0, random forest, support vector machines, and naive Bayes to support users in developing predictive models using EHRs. It then makes the analytics results accessible through an interactive visual interface. By integrating different sampling strategies, analytics algorithms, visualization techniques, and human-data interaction, VERONICA assists users in comparing prediction models in a systematic way. To demonstrate the usefulness and utility of our proposed system, we use the clinical dataset stored at ICES to identify the best representative feature groups in detecting patients who are at high risk of developing acute kidney injury.

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Machine Learning Frameworks in Cancer Detection

E3S Web of Conferences ◽

10.1051/e3sconf/202129701073 ◽

2021 ◽

Vol 297 ◽

pp. 01073

Author(s):

Sabyasachi Pramanik ◽

K. Martin Sagayam ◽

Om Prakash Jena

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Cancer Development ◽

Support Vector ◽

Learning Approaches ◽

Learning Techniques ◽

Fact Finding ◽

Risk Of Cancer

Cancer has been described as a diverse illness with several distinct subtypes that may occur simultaneously. As a result, early detection and forecast of cancer types have graced essentially in cancer fact-finding methods since they may help to improve the clinical treatment of cancer survivors. The significance of categorizing cancer suffers into higher or lower-threat categories has prompted numerous fact-finding associates from the bioscience and genomics field to investigate the utilization of machine learning (ML) algorithms in cancer diagnosis and treatment. Because of this, these methods have been used with the goal of simulating the development and treatment of malignant diseases in humans. Furthermore, the capacity of machine learning techniques to identify important characteristics from complicated datasets demonstrates the significance of these technologies. These technologies include Bayesian networks and artificial neural networks, along with a number of other approaches. Decision Trees and Support Vector Machines which have already been extensively used in cancer research for the creation of predictive models, also lead to accurate decision making. The application of machine learning techniques may undoubtedly enhance our knowledge of cancer development; nevertheless, a sufficient degree of validation is required before these approaches can be considered for use in daily clinical practice. An overview of current machine learning approaches utilized in the simulation of cancer development is presented in this paper. All of the supervised machine learning approaches described here, along with a variety of input characteristics and data samples, are used to build the prediction models. In light of the increasing trend towards the use of machine learning methods in biomedical research, we offer the most current papers that have used these approaches to predict risk of cancer or patient outcomes in order to better understand cancer.

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Using machine learning techniques to develop prediction models for detecting unpaid credit card customers

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189080 ◽

2020 ◽

Vol 39 (5) ◽

pp. 6073-6087

Author(s):

Meltem Yontar ◽

Özge Hüsniye Namli ◽

Seda Yanik

Keyword(s):

Decision Tree ◽

Credit Card ◽

Banking Sector ◽

Performance Metrics ◽

Prediction Models ◽

Regression Tree ◽

Classification And Regression Tree ◽

Machine Learning Techniques ◽

Support Vector ◽

Cart Algorithm

Customer behavior prediction is gaining more importance in the banking sector like in any other sector recently. This study aims to propose a model to predict whether credit card users will pay their debts or not. Using the proposed model, potential unpaid risks can be predicted and necessary actions can be taken in time. For the prediction of customers’ payment status of next months, we use Artificial Neural Network (ANN), Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5, which are widely used artificial intelligence and decision tree algorithms. Our dataset includes 10713 customer’s records obtained from a well-known bank in Taiwan. These records consist of customer information such as the amount of credit, gender, education level, marital status, age, past payment records, invoice amount and amount of credit card payments. We apply cross validation and hold-out methods to divide our dataset into two parts as training and test sets. Then we evaluate the algorithms with the proposed performance metrics. We also optimize the parameters of the algorithms to improve the performance of prediction. The results show that the model built with the CART algorithm, one of the decision tree algorithm, provides high accuracy (about 86%) to predict the customers’ payment status for next month. When the algorithm parameters are optimized, classification accuracy and performance are increased.

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Sentiment Analysis Techniques and Application-Survey and Taxonomy

Engineering and Technology Journal ◽

10.47191/etj/v6i1.01 ◽

2021 ◽

Vol 04 (01) ◽

Author(s):

Mahmood Umar ◽

Keyword(s):

Machine Learning ◽

Sentiment Analysis ◽

Sentiment Classification ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Data Generation ◽

Analysis Techniques ◽

Learning Techniques ◽

Document Level

Nowadays, social media platforms, blogs, and e-commerce are commonly use to express opinion on politics, movies, products, education respectively; for election forecasting, business boosting and improvement of teaching and learning. As a result, data generation becomes easier; producing big data which requires appropriate techniques and tools to analyse easily, accurately and timely. Thus, making sentiment analysis very demanding research area. This study will investigate on what basis (sentiment classification level) or area of application (data source) do supervised machine learning approaches particularly Support Vector Machine (SVM), Naïve Bayes, and Maximum Entropy algorithms, and other technique-lexicon-based approach give the best result in sentiment analysis. Based on the review of the literature there is a contradiction on the point that SVM generated the best result in analyzing student sentiment on document level. This study also discovers that sentiment analysis differs from system to system based on polarity (types of the classes to predict: positive or negative, subjective or objective), different levels of classification (sentence, phrase, or document level) and language that is processed. This research produces a taxonomy which serves as a guide for the choice of techniques in sentiment analysis. The taxonomy explores the sentiment classification levels and data preprocessing stages. It also explores that sentiment analysis techniques were organised in to three (3) groups; Machine learning, Lexicon and hybrid or combination. The machine learning techniques were sub-grouped in to two (2) namely; supervised and unsupervised. The supervised were organized in to two (2): Classification and Regression. un-supervised machine learning techniques includes clustering and association. The clustering technique consist of k-means. Decision tree which is a classification based under supervised type of machine learning technique consist of random forest,(Akinkunmi, 2019) while the ruled-based classifiers consist of confidence criterion and support criterion. The commonly used tools are Weka, Python compiler, and R programming tool.

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Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

Vision The Journal of Business Perspective ◽

10.1177/0972262918821221 ◽

2019 ◽

Vol 23 (1) ◽

pp. 12-21 ◽

Cited By ~ 2

Author(s):

Shikha N. Khera ◽

Divya

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Confusion Matrix ◽

Predictive Modelling ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

It Industry ◽

Knowledge Based ◽

Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

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Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18052584 ◽

2021 ◽

Vol 18 (5) ◽

pp. 2584

Author(s):

Cheng-Chien Lai ◽

Wei-Hsin Huang ◽

Betty Chia-Chen Chang ◽

Lee-Ching Hwang

Keyword(s):

Machine Learning ◽

Smoking Cessation ◽

Success Rate ◽

Prediction Models ◽

Smoking Status ◽

Medical Center ◽

Machine Learning Algorithms ◽

Classification And Regression Tree ◽

Support Vector ◽

Smoking Cessation Outcome

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

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Sentiment Analysis using various Machine Learning and Deep Learning Techniques

Journal of the Nigerian Society of Physical Sciences ◽

10.46481/jnsps.2021.308 ◽

2021 ◽

pp. 385-394

Author(s):

V Umarani ◽

A Julian ◽

J Deepa

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Naive Bayes ◽

Naïve Bayes ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Analysis Process ◽

Learning Techniques

Sentiment analysis has gained a lot of attention from researchers in the last year because it has been widely applied to a variety of application domains such as business, government, education, sports, tourism, biomedicine, and telecommunication services. Sentiment analysis is an automated computational method for studying or evaluating sentiments, feelings, and emotions expressed as comments, feedbacks, or critiques. The sentiment analysis process can be automated using machine learning techniques, which analyses text patterns faster. The supervised machine learning technique is the most used mechanism for sentiment analysis. The proposed work discusses the flow of sentiment analysis process and investigates the common supervised machine learning techniques such as multinomial naive bayes, Bernoulli naive bayes, logistic regression, support vector machine, random forest, K-nearest neighbor, decision tree, and deep learning techniques such as Long Short-Term Memory and Convolution Neural Network. The work examines such learning methods using standard data set and the experimental results of sentiment analysis demonstrate the performance of various classifiers taken in terms of the precision, recall, F1-score, RoC-Curve, accuracy, running time and k fold cross validation and helps in appreciating the novelty of the several deep learning techniques and also giving the user an overview of choosing the right technique for their application.

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THE EFFICIENCY OF ENSEMBLE CLASSIFIERS IN PREDICTING THE JOHANNESBURG STOCK EXCHANGE ALL-SHARE INDEX DIRECTION

Journal of Financial Management Markets and Institutions ◽

10.1142/s2282717x19500014 ◽

2019 ◽

Vol 07 (02) ◽

pp. 1950001

Author(s):

THABANG MOKOALELI-MOKOTELI ◽

SHAUN RAMSUMAR ◽

HIMA VADAPALLI

Keyword(s):

Logistic Regression ◽

Stock Market ◽

Prediction Models ◽

Stock Exchange ◽

Machine Learning Techniques ◽

Ensemble Prediction ◽

Support Vector ◽

Ensemble Classifiers ◽

Ensemble Models ◽

Johannesburg Stock Exchange

The success of investors in obtaining huge financial rewards from the stock market depends on their ability to predict the direction of the stock market index. The purpose of this study is to evaluate the efficacy of several ensemble prediction models (Boosted, RUS-Boosted, Subspace Disc, Bagged, and Subspace KNN) in predicting the daily direction of the Johannesburg Stock Exchange (JSE) All-Share index compared to other commonly used machine learning techniques including support vector machines (SVM), logistic regression and [Formula: see text]-nearest neighbor (KNN). The findings in this study show that, among all ensemble models, Boosted algorithm is the best performer followed by RUS-Boosted. When compared to the other techniques, ensemble technique (represented by Boosted) outperformed these techniques, followed by KNN, logistic regression and SVM, respectively. These findings suggest that investors should include ensemble models among the index prediction models if they want to make huge profits in the stock markets. However, not all investors can benefit from this as models may suffer from alpha decay as more and more investors use them, implying that the successful algorithms have limited shelf life.

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Improved Intrusion Detection Algorithm based on TLBO and GA Algorithms

The International Arab Journal of Information Technology ◽

10.34028/iajit/18/2/5 ◽

2021 ◽

Vol 18 (2) ◽

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Optimization Algorithm ◽

Feature Subset Selection ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Feature Subset ◽

Teaching Learning Based Optimization ◽

Teaching Learning

Optimization algorithms are widely used for the identification of intrusion. This is attributable to the increasing number of audit data features and the decreasing performance of human-based smart Intrusion Detection Systems (IDS) regarding classification accuracy and training time. In this paper, an improved method for intrusion detection for binary classification was presented and discussed in detail. The proposed method combined the New Teaching-Learning-Based Optimization Algorithm (NTLBO), Support Vector Machine (SVM), Extreme Learning Machine (ELM), and Logistic Regression (LR) (feature selection and weighting) NTLBO algorithm with supervised machine learning techniques for Feature Subset Selection (FSS). The process of selecting the least number of features without any effect on the result accuracy in FSS was considered a multi-objective optimization problem. The NTLBO was proposed in this paper as an FSS mechanism; its algorithm-specific, parameter-less concept (which requires no parameter tuning during an optimization) was explored. The experiments were performed on the prominent intrusion machine-learning datasets (KDDCUP’99 and CICIDS 2017), where significant enhancements were observed with the suggested NTLBO algorithm as compared to the classical Teaching-Learning-Based Optimization algorithm (TLBO), NTLBO presented better results than TLBO and many existing works. The results showed that NTLBO reached 100% accuracy for KDDCUP’99 dataset and 97% for CICIDS dataset

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Classification models using circulating neutrophil transcripts can detect unruptured intracranial aneurysm

Journal of Translational Medicine ◽

10.1186/s12967-020-02550-2 ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Kerry E. Poppenberg ◽

Vincent M. Tutino ◽

Lu Li ◽

Muhammad Waqas ◽

Armond June ◽

...

Keyword(s):

Machine Learning ◽

Random Forest ◽

Prediction Models ◽

Model Performance ◽

Supervised Machine Learning ◽

Support Vector ◽

Learning Methods ◽

Training Cohort ◽

Network Analyses ◽

Machine Learning Methods

Abstract Background Intracranial aneurysms (IAs) are dangerous because of their potential to rupture. We previously found significant RNA expression differences in circulating neutrophils between patients with and without unruptured IAs and trained machine learning models to predict presence of IA using 40 neutrophil transcriptomes. Here, we aim to develop a predictive model for unruptured IA using neutrophil transcriptomes from a larger population and more robust machine learning methods. Methods Neutrophil RNA extracted from the blood of 134 patients (55 with IA, 79 IA-free controls) was subjected to next-generation RNA sequencing. In a randomly-selected training cohort (n = 94), the Least Absolute Shrinkage and Selection Operator (LASSO) selected transcripts, from which we constructed prediction models via 4 well-established supervised machine-learning algorithms (K-Nearest Neighbors, Random Forest, and Support Vector Machines with Gaussian and cubic kernels). We tested the models in the remaining samples (n = 40) and assessed model performance by receiver-operating-characteristic (ROC) curves. Real-time quantitative polymerase chain reaction (RT-qPCR) of 9 IA-associated genes was used to verify gene expression in a subset of 49 neutrophil RNA samples. We also examined the potential influence of demographics and comorbidities on model prediction. Results Feature selection using LASSO in the training cohort identified 37 IA-associated transcripts. Models trained using these transcripts had a maximum accuracy of 90% in the testing cohort. The testing performance across all methods had an average area under ROC curve (AUC) = 0.97, an improvement over our previous models. The Random Forest model performed best across both training and testing cohorts. RT-qPCR confirmed expression differences in 7 of 9 genes tested. Gene ontology and IPA network analyses performed on the 37 model genes reflected dysregulated inflammation, cell signaling, and apoptosis processes. In our data, demographics and comorbidities did not affect model performance. Conclusions We improved upon our previous IA prediction models based on circulating neutrophil transcriptomes by increasing sample size and by implementing LASSO and more robust machine learning methods. Future studies are needed to validate these models in larger cohorts and further investigate effect of covariates.

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Using Random Forests on Real-World City Data for Urban Planning in a Visual Semantic Decision Support System

Sensors ◽

10.3390/s19102266 ◽

2019 ◽

Vol 19 (10) ◽

pp. 2266 ◽

Cited By ~ 1

Author(s):

Nikolaos Sideris ◽

Georgios Bardis ◽

Athanasios Voulodimos ◽

Georgios Miaoulis ◽

Djamchid Ghazanfarpour

Keyword(s):

Machine Learning ◽

Urban Planning ◽

Random Forests ◽

Real World ◽

Performance Metrics ◽

World City ◽

Supervised Machine Learning ◽

Machine Learning Techniques ◽

Support Vector ◽

Real World Data

The constantly increasing amount and availability of urban data derived from varying sources leads to an assortment of challenges that include, among others, the consolidation, visualization, and maximal exploitation prospects of the aforementioned data. A preeminent problem affecting urban planning is the appropriate choice of location to host a particular activity (either commercial or common welfare service) or the correct use of an existing building or empty space. In this paper, we propose an approach to address these challenges availed with machine learning techniques. The proposed system combines, fuses, and merges various types of data from different sources, encodes them using a novel semantic model that can capture and utilize both low-level geometric information and higher level semantic information and subsequently feeds them to the random forests classifier, as well as other supervised machine learning models for comparisons. Our experimental evaluation on multiple real-world data sets comparing the performance of several classifiers (including Feedforward Neural Networks, Support Vector Machines, Bag of Decision Trees, k-Nearest Neighbors and Naïve Bayes), indicated the superiority of Random Forests in terms of the examined performance metrics (Accuracy, Specificity, Precision, Recall, F-measure and G-mean).

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