scholarly journals Improvement of Adequate Digoxin Dosage: An Application of Machine Learning Approach

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ya-Han Hu ◽  
Chun-Tien Tai ◽  
Chih-Fong Tsai ◽  
Min-Wei Huang
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Prediction Models ◽  
Medical Center ◽  
Laboratory Data ◽  
Regression Tree ◽  
Digoxin Concentration ◽  
Classification And Regression Tree ◽  
Machine Learning Techniques ◽  
Learning Techniques

Digoxin is a high-alert medication because of its narrow therapeutic range and high drug-to-drug interactions (DDIs). Approximately 50% of digoxin toxicity cases are preventable, which motivated us to improve the treatment outcomes of digoxin. The objective of this study is to apply machine learning techniques to predict the appropriateness of initial digoxin dosage. A total of 307 inpatients who had their conditions treated with digoxin between 2004 and 2013 at a medical center in Taiwan were collected in the study. Ten independent variables, including demographic information, laboratory data, and whether the patients had CHF were also noted. A patient with serum digoxin concentration being controlled at 0.5–0.9 ng/mL after his/her initial digoxin dosage was defined as having an appropriate use of digoxin; otherwise, a patient was defined as having an inappropriate use of digoxin. Weka 3.7.3, an open source machine learning software, was adopted to develop prediction models. Six machine learning techniques were considered, including decision tree (C4.5), k-nearest neighbors (kNN), classification and regression tree (CART), randomForest (RF), multilayer perceptron (MLP), and logistic regression (LGR). In the non-DDI group, the area under ROC curve (AUC) of RF (0.912) was excellent, followed by that of MLP (0.813), CART (0.791), and C4.5 (0.784); the remaining classifiers performed poorly. For the DDI group, the AUC of RF (0.892) was the best, followed by CART (0.795), MLP (0.777), and C4.5 (0.774); the other classifiers’ performances were less than ideal. The decision tree-based approaches and MLP exhibited markedly superior accuracy performance, regardless of DDI status. Although digoxin is a high-alert medication, its initial dose can be accurately determined by using data mining techniques such as decision tree-based and MLP approaches. Developing a dosage decision support system may serve as a supplementary tool for clinicians and also increase drug safety in clinical practice.

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

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.

scholarly journals Applications of machine learning techniques to predict filariasis using socio-economic factors

2019 ◽  
Vol 147 ◽  
Author(s):  
Phani Krishna Kondeti ◽  
Kumar Ravi ◽  
Srinivasa Rao Mutheneni ◽  
Madhusudhan Rao Kadiri ◽  
Sriram Kumaraswamy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Probabilistic Neural Network ◽  
Decision Rules ◽  
Regression Tree ◽  
Warning System ◽  
Pilot Scale ◽  
Classification And Regression Tree ◽  
Machine Learning Techniques ◽  
Gradient Boosting ◽  
Learning Techniques

Abstract Filariasis is one of the major public health concerns in India. Approximately 600 million people spread across 250 districts of India are at risk of filariasis. To predict this disease, a pilot scale study was carried out in 30 villages of Karimnagar district of Telangana from 2004 to 2007 to collect epidemiological and socio-economic data. The collected data are analysed by employing various machine learning techniques such as Naïve Bayes (NB), logistic model tree, probabilistic neural network, J48 (C4.5), classification and regression tree, JRip and gradient boosting machine. The performances of these algorithms are reported using sensitivity, specificity, accuracy and area under ROC curve (AUC). Among all employed classification methods, NB yielded the best AUC of 64% and was equally statistically significant with the rest of the classifiers. Similarly, the J48 algorithm generated 23 decision rules that help in developing an early warning system to implement better prevention and control efforts in the management of filariasis.

A Review of Statistical and Machine Learning Techniques for Microvascular Complications in Type 2 Diabetes

2020 ◽  
Vol 16 ◽  
Author(s):  
Nitigya Sambyal ◽  
Poonam Saini ◽  
Rupali Syal
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Clinical Medicine ◽  
Microvascular Complications ◽  
Descriptive Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
Public Health Issue ◽  
Learning Techniques ◽  
Health Organization

Background and Introduction: Diabetes mellitus is a metabolic disorder that has emerged as a serious public health issue worldwide. According to the World Health Organization (WHO), without interventions, the number of diabetic incidences is expected to be at least 629 million by 2045. Uncontrolled diabetes gradually leads to progressive damage to eyes, heart, kidneys, blood vessels and nerves. Method: The paper presents a critical review of existing statistical and Artificial Intelligence (AI) based machine learning techniques with respect to DM complications namely retinopathy, neuropathy and nephropathy. The statistical and machine learning analytic techniques are used to structure the subsequent content review. Result: It has been inferred that statistical analysis can help only in inferential and descriptive analysis whereas, AI based machine learning models can even provide actionable prediction models for faster and accurate diagnose of complications associated with DM. Conclusion: The integration of AI based analytics techniques like machine learning and deep learning in clinical medicine will result in improved disease management through faster disease detection and cost reduction for disease treatment.

scholarly journals Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

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.

scholarly journals Binary Class Classification of Software Faults in Software Modules using Popular Machine Learning Techniques

2020 ◽  
Vol 6 (6) ◽  
pp. 14-18
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Regression Tree ◽  
Machine Learning Techniques ◽  
Classification Algorithms ◽  
Customer Requirements ◽  
Software Faults ◽  
Learning Techniques ◽  
Software Modules

Software engineering is an important area that deals with development and maintenance of software. After developing a software, it is always important to track its performance. One has to always see whether the software functions according to customer requirements. To ensure this, faulty and non- faulty modules must be identified. For this purpose, one can make use of a model for binary class classification of faults. Different technique's outputs differ in one or the other way with respect to the following: fault dataset used, complexity, classification algorithm implemented, etc. Various machine learning techniques can be used for this purpose. But this paper deals with the best classification algorithms available till date and they are decision tree, random forest, naive bayes and logistic regression (tree-based techniques and bayesian based techniques). The motive behind developing such a project is to identify the faulty modules within a software before the actual software testing takes place. As a result, the time consumed by testers or the workload of the testers can be reduced to an extent. This work is very well useful to those working in software industry and also to those people carrying out research in software engineering where the lifecycle of development of a software is discussed.

scholarly journals Survival prediction models since liver transplantation - comparisons between Cox models and machine learning techniques

2020 ◽  
Author(s):  
Georgios Kantidakis ◽  
Hein Putter ◽  
Carlo Lancia ◽  
Jacob de Boer ◽  
Andries E Braat ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Liver Transplantation ◽  
Prediction Models ◽  
Machine Learning Techniques ◽  
Brier Score ◽  
Survival Prediction ◽  
Cox Models ◽  
Learning Techniques ◽  
Random Survival Forest

Abstract Background: Predicting survival of recipients after liver transplantation is regarded as one of the most important challenges in contemporary medicine. Hence, improving on current prediction models is of great interest.Nowadays, there is a strong discussion in the medical field about machine learning (ML) and whether it has greater potential than traditional regression models when dealing with complex data. Criticism to ML is related to unsuitable performance measures and lack of interpretability which is important for clinicians.Methods: In this paper, ML techniques such as random forests and neural networks are applied to large data of 62294 patients from the United States with 97 predictors selected on clinical/statistical grounds, over more than 600, to predict survival from transplantation. Of particular interest is also the identification of potential risk factors. A comparison is performed between 3 different Cox models (with all variables, backward selection and LASSO) and 3 machine learning techniques: a random survival forest and 2 partial logistic artificial neural networks (PLANNs). For PLANNs, novel extensions to their original specification are tested. Emphasis is given on the advantages and pitfalls of each method and on the interpretability of the ML techniques.Results: Well-established predictive measures are employed from the survival field (C-index, Brier score and Integrated Brier Score) and the strongest prognostic factors are identified for each model. Clinical endpoint is overall graft-survival defined as the time between transplantation and the date of graft-failure or death. The random survival forest shows slightly better predictive performance than Cox models based on the C-index. Neural networks show better performance than both Cox models and random survival forest based on the Integrated Brier Score at 10 years.Conclusion: In this work, it is shown that machine learning techniques can be a useful tool for both prediction and interpretation in the survival context. From the ML techniques examined here, PLANN with 1 hidden layer predicts survival probabilities the most accurately, being as calibrated as the Cox model with all variables.

scholarly journals Machine Learning Frameworks in Cancer Detection

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.

Improved argumentative paragraphs detection in academic theses supported with unit segmentation

2021 ◽  
pp. 1-11
Author(s):  
Jesús Miguel García-Gorrostieta ◽  
Aurelio López-López ◽  
Samuel González-López ◽  
Adrián Pastor López-Monroy
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Automatic Detection ◽  
Machine Learning Techniques ◽  
Svm Classifier ◽  
Complex Task ◽  
Decision Tree Classifier ◽  
Learning Techniques ◽  
Tree Classifier ◽  
Academic Author

Academic theses writing is a complex task that requires the author to be skilled in argumentation. The goal of the academic author is to communicate clear ideas and to convince the reader of the presented claims. However, few students are good arguers, and this is a skill that takes time to master. In this paper, we present an exploration of lexical features used to model automatic detection of argumentative paragraphs using machine learning techniques. We present a novel proposal, which combines the information in the complete paragraph with the detection of argumentative segments in order to achieve improved results for the detection of argumentative paragraphs. We propose two approaches; a more descriptive one, which uses the decision tree classifier with indicators and lexical features; and another more efficient, which uses an SVM classifier with lexical features and a Document Occurrence Representation (DOR). Both approaches consider the detection of argumentative segments to ensure that a paragraph detected as argumentative has indeed segments with argumentation. We achieved encouraging results for both approaches.

Analysis of Kinase Inhibitors and Druggability of Kinase-Targets Using Machine Learning Techniques

2012 ◽  
pp. 155-165
Author(s):  
S. Prasanthi ◽  
S.Durga Bhavani ◽  
T. Sobha Rani ◽  
Raju S. Bapi
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Kinase Inhibitors ◽  
Kinase Inhibitor ◽  
Classification Problem ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Decision Tree Classifier ◽  
Data Set ◽  
Learning Techniques

Vast majority of successful drugs or inhibitors achieve their activity by binding to, and modifying the activity of a protein leading to the concept of druggability. A target protein is druggable if it has the potential to bind the drug-like molecules. Hence kinase inhibitors need to be studied to understand the specificity of a kinase inhibitor in choosing a particular kinase target. In this paper we focus on human kinase drug target sequences since kinases are known to be potential drug targets. Also we do a preliminary analysis of kinase inhibitors in order to study the problem in the protein-ligand space in future. The identification of druggable kinases is treated as a classification problem in which druggable kinases are taken as positive data set and non-druggable kinases are chosen as negative data set. The classification problem is addressed using machine learning techniques like support vector machine (SVM) and decision tree (DT) and using sequence-specific features. One of the challenges of this classification problem is due to the unbalanced data with only 48 druggable kinases available against 509 non-drugggable kinases present at Uniprot. The accuracy of the decision tree classifier obtained is 57.65 which is not satisfactory. A two-tier architecture of decision trees is carefully designed such that recognition on the non-druggable dataset also gets improved. Thus the overall model is shown to achieve a final performance accuracy of 88.37. To the best of our knowledge, kinase druggability prediction using machine learning approaches has not been reported in literature.

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