scholarly journals 372 Machine learning and pulmonary hypertension

2021 ◽  
Vol 23 (Supplement_G) ◽  
Author(s):  
Lucia Soriente ◽  
Luigi Cerulo ◽  
Giovanna Mercurio ◽  
Giuseppe Iuliano ◽  
Francesco Paolo Mancini
Keyword(s):  
Machine Learning ◽  
Pulmonary Hypertension ◽  
Decision Tree ◽  
Learning Algorithm ◽  
Right Atrial ◽  
Validation Dataset ◽  
Machine Learning Algorithm ◽  
Acceleration Time ◽  
Axial Deviation ◽  
The Right

Abstract Aims Pulmonary hypertension (IP) characterized by an average resting pulmonary pressure ≥20 mmHg can sustain various clinical conditions that differ in physiopathological, haemodynamic, and therefore therapeutic aspects. The goal of our work was to apply a machine learning algorithm that could accurately distinguish pre- and post-heart pulmonary hypertension through non-invasive methods (medical history, clinical, and echocardiographic data). Methods and results In order to achieve our goal we used the ‘decision tree’ machine learning algorithm implemented in the C5.0 package of the R development environment. The first step was the preparation of the data. The dataset of patients with IP was composed of 85 patients divided into XX precapillary IP (1) and YY postcapillary (2). Each patient is described by 11 features: some comorbidities (arterial hypertension and atrial fibrillation), BMI, right axial deviation on ECG, DLCO, and some echocardiographic measurements (e/e′, right atrial area, S wave at TDI, acceleration time on the pulmonary, inferior vena cava, diameters of the right ventricle). The dataset was divided into a data.train training subset (45 patients) and an evaluation subset (40 patients), maintaining the proportion between classes. Starting from the training dataset, the C5.0 algorithm generated the decision tree shown in Figure 1. The root node was made up of the mitral pattern e/e′, followed by the right axis deviation on the ECG and the acceleration rate on the lung that the algorithm considered the best discriminated features. The model was then validated in the validation dataset and through the Caret package and the Confusion matrix function we calculated the performance metrics of the algorithm obtaining an accuracy of 0.87, a kappa statistic of 0.742, a sensitivity of 0.913, and a specificity of 0.823. The true positive rate was 0.87 while the true negative rate was 0.87. The performance of the model was also measured using the ROC curve, obtaining an area under the curve of 0.916. Conclusions Our results show that the ‘decision tree’ algorithm starting from echocardiographic data and the ECG has a good ability to discriminate between the precapillary and postcapillary IP. In particular, the decision chain consisting of: mitral pattern and / and ratio ≤8, right axial deviation on the ECG and acceleration time on the lung ≤80 ms seems to predict the IP class with reasonable accuracy. Our results confirm that the probability of prediction and the prediction itself depend, however, on what degree of purity the partitions learned during the decision tree construction process are made up. To improve the estimation of the algorithm’s performance and thus generalize the results obtained, we believe to evaluate this approach on larger datasets also considering different machine learning algorithms. 372 Figure

scholarly journals Predicting Bank Operational Efficiency Using Machine Learning Algorithm: Comparative Study of Decision Tree, Random Forest, and Neural Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Peter Appiahene ◽  
Yaw Marfo Missah ◽  
Ussiph Najim
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Banking Sector ◽  
Banking Industry ◽  
Predictive Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Machine Learning Algorithm ◽  
And Performance

The financial crisis that hit Ghana from 2015 to 2018 has raised various issues with respect to the efficiency of banks and the safety of depositors’ in the banking industry. As part of measures to improve the banking sector and also restore customers’ confidence, efficiency and performance analysis in the banking industry has become a hot issue. This is because stakeholders have to detect the underlying causes of inefficiencies within the banking industry. Nonparametric methods such as Data Envelopment Analysis (DEA) have been suggested in the literature as a good measure of banks’ efficiency and performance. Machine learning algorithms have also been viewed as a good tool to estimate various nonparametric and nonlinear problems. This paper presents a combined DEA with three machine learning approaches in evaluating bank efficiency and performance using 444 Ghanaian bank branches, Decision Making Units (DMUs). The results were compared with the corresponding efficiency ratings obtained from the DEA. Finally, the prediction accuracies of the three machine learning algorithm models were compared. The results suggested that the decision tree (DT) and its C5.0 algorithm provided the best predictive model. It had 100% accuracy in predicting the 134 holdout sample dataset (30% banks) and a P value of 0.00. The DT was followed closely by random forest algorithm with a predictive accuracy of 98.5% and a P value of 0.00 and finally the neural network (86.6% accuracy) with a P value 0.66. The study concluded that banks in Ghana can use the result of this study to predict their respective efficiencies. All experiments were performed within a simulation environment and conducted in R studio using R codes.

scholarly journals Accurate Integrated System to detect Pulmonary and Extra Pulmonary Tuberculosis using Machine Learning Algorithms

2021 ◽  
Vol 24 (68) ◽  
pp. 104-122
Author(s):  
Rupinder Kaur ◽  
Anurag Sharma
Keyword(s):  
Machine Learning ◽  
Pulmonary Tuberculosis ◽  
Decision Tree ◽  
Learning Algorithm ◽  
Learning Algorithms ◽  
Integrated System ◽  
Machine Learning Algorithms ◽  
Validation Dataset ◽  
Extra Pulmonary Tuberculosis ◽  
Extra Pulmonary

Several studies have been reported the use of machine learning algorithms in the detection of Tuberculosis, but studies that discuss the detection of both types of TB, i.e., Pulmonary and Extra Pulmonary Tuberculosis, using machine learning algorithms are lacking. Therefore, an integrated system based on machine learning models has been proposed in this paper to assist doctors and radiologists in interpreting patients’ data to detect of PTB and EPTB. Three basic machine learning algorithms, Decision Tree, Naïve Bayes, SVM, have been used to predict and compare their performance. The clinical data and the image data are used as input to the models and these datasets have been collected from various hospitals of Jalandhar, Punjab, India. The dataset used to train the model comprises 200 patients’ data containing 90 PTB patients, 67 EPTB patients, and 43 patients having NO TB. The validation dataset contains 49 patients, which exhibited the best accuracy of 95% for classifying PTB and EPTB using Decision Tree, a machine learning algorithm.

scholarly journals Decision Tree With Only Two Musculoskeletal Sites to Diagnose Polymyalgia Rheumatica Using [18F]FDG PET-CT

2021 ◽  
Vol 8 ◽  
Author(s):  
Anthime Flaus ◽  
Julie Amat ◽  
Nathalie Prevot ◽  
Louis Olagne ◽  
Lucie Descamps ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Polymyalgia Rheumatica ◽  
Fdg Pet ◽  
Validation Cohort ◽  
Learning Algorithm ◽  
Final Diagnosis ◽  
Machine Learning Algorithm ◽  
Pet Ct ◽  
Fdg Pet Ct

Introduction: The aim of this study was to find the best ordered combination of two FDG positive musculoskeletal sites with a machine learning algorithm to diagnose polymyalgia rheumatica (PMR) vs. other rheumatisms in a cohort of patients with inflammatory rheumatisms.Methods: This retrospective study included 140 patients who underwent [18F]FDG PET-CT and whose final diagnosis was inflammatory rheumatism. The cohort was randomized, stratified on the final diagnosis into a training and a validation cohort. FDG uptake of 17 musculoskeletal sites was evaluated visually and set positive if uptake was at least equal to that of the liver. A decision tree classifier was trained and validated to find the best combination of two positives sites to diagnose PMR. Diagnosis performances were measured first, for each musculoskeletal site, secondly for combination of two positive sites and thirdly using the decision tree created with machine learning.Results: 55 patients with PMR and 85 patients with other inflammatory rheumatisms were included. Musculoskeletal sites, used either individually or in combination of two, were highly imbalanced to diagnose PMR with a high specificity and a low sensitivity. The machine learning algorithm identified an optimal ordered combination of two sites to diagnose PMR. This required a positive interspinous bursa or, if negative, a positive trochanteric bursa. Following the decision tree, sensitivity and specificity to diagnose PMR were respectively 73.2 and 87.5% in the training cohort and 78.6 and 80.1% in the validation cohort.Conclusion: Ordered combination of two visually positive sites leads to PMR diagnosis with an accurate sensitivity and specificity vs. other rheumatisms in a large cohort of patients with inflammatory rheumatisms.

scholarly journals Phishing Scam Detection using Machine Learning

2019 ◽  
Vol 9 (1S) ◽  
pp. 114-118
Keyword(s):  
Machine Learning ◽  
Decision Tree ◽  
Learning Algorithm ◽  
The Internet ◽  
Machine Learning Algorithm ◽  
Learning Approaches ◽  
Decision Tree Algorithm ◽  
Sensitive Data ◽  
Tree Algorithm ◽  
Testing Issue

As a wrongdoing of utilizing specialized intends to take sensitive data of clients and users in the internet, phishing is as of now an advanced risk confronting the Internet, and misfortunes due to phishing are developing consistently. Recognition of these phishing scams is a very testing issue on the grounds that phishing is predominantly a semantics based assault, which particularly manhandles human vulnerabilities, anyway not system or framework vulnerabilities. Phishing costs. As a product discovery plot, two primary methodologies are generally utilized: blacklists/whitelists and machine learning approaches. Every phishing technique has different parameters and type of attack. Using decision tree algorithm we find out whether the attack is legitimate or a scam. We measure this by grouping them with diverse parameters and features, thereby assisting the machine learning algorithm to edify.

scholarly journals Heart Disease Prediction Using Machine Learning Algorithm

2019 ◽  
Vol 8 (10) ◽  
pp. 2603-2606
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Tree ◽  
Learning Algorithm ◽  
Heart Diseases ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Tree Classifier

Heart disease is a common problem which can be very severe in old ages and also in people not having a healthy lifestyle. With regular check-up and diagnosis in addition to maintaining a decent eating habit can prevent it to some extent. In this paper we have tried to implement the most sought after and important machine learning algorithm to predict the heart disease in a patient. The decision tree classifier is implemented based on the symptoms which are specifically the attributes required for the purpose of prediction. Using the decision tree algorithm, we will be able to identify those attributes which are the best one that will lead us to a better prediction of the datasets. The decision tree algorithm works in a way where it tries to solve the problem by the help of tree representation. Here each internal node of the tree represents an attribute, and each leaf node corresponds to a class label. The support vector machine algorithm helps us to classify the datasets on the basis of kernel and it also groups the dataset using hyperplane. The main objective of this project is to try and reduce the number of occurrences of the heart diseases in patients

scholarly journals Machine learning prediction on number of patient due to conjunctivitis based on air pollutants: A preliminary study

2020 ◽  
Author(s):  
Juan Chen ◽  
Yong-ran Cheng ◽  
Zhan-hui Feng ◽  
Meng-Yun Zhou ◽  
Nan Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Decision Tree ◽  
Air Pollutants ◽  
Learning Algorithm ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Adequate Treatment ◽  
Number Of Patients

Abstract Background: Accurate prediction of the number of patients with conjunctivitis plays an important role in providing adequate treatment at the hospital, but such accurate predictive model currently does not exist. The current study sought to use machine learning (ML) prediction based on past patient for conjunctivitis and several air pollutants. The optimal machine learning prediction model was selected to predict conjunctivitis-related number patients.Methods: The average daily air pollutants concentrations (CO, O3, NO2, SO2, PM10, PM2.5) and weather data (highest and lowest temperature) were collected. Data were randomly divided into training dataset and test dataset, and normalized mean square error (NMSE) was calculated by 10 fold cross validation, comparing between the ability of seven ML methods to predict the number of patient due to conjunctivitis (Lasso penalized liner model, Decision tree, Boosting regression, Bagging regression, Random forest, Support vector, and Neural network). According to the accuracy of impact prediction, the important air and weather factors that affect conjunctivitis were identified.Results: A total of 84977 cases to treat conjunctivitis were obtained from the ophthalmology center of the Affiliated Hospital of Hangzhou Normal University. For all patients together, the NMSE of the different methods were as follows: Lasso penalized liner regression: 0.755, Decision tree: 0.710, Boosting regression: 0.616, Bagging regression: 0.615, Random forest: 0.392, Support vectors: 0.688, and Neural network: 0.476. Further analyses, stratified by gender and age at diagnosis, supported Random forest as being superior to others ML methods. The main factors affecting conjunctivitis were: O3, NO2, SO2 and air temperature.Conclusion: Machine learning algorithm can predict number of patients due to conjunctivitis, among which, the Random forest algorithm had the highest accuracy. Machine learning algorithm could provide accurate information for hospitals dealing with conjunctivitis caused by air factors.

scholarly journals A Solution to Cartpole using Neural Networks and Tensorflow

2019 ◽  
Vol 8 (4) ◽  
pp. 920-924
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Logistic Regression ◽  
Computational Efficiency ◽  
Learning Algorithm ◽  
Machine Learning Techniques ◽  
Machine Learning Algorithm ◽  
Learning Techniques ◽  
The Right ◽  
Algorithm Benchmarking

Machine learning is not quite a new topic for discussion these days. A lot of enthusiasts excel in this field. The problem just lies with the beginners who lack just the right amount of intuition in to step ahead in this field. This paper is all about finding a simple enough solution to this issue through an example problem Cart-Pole an Open AI Gym’s classic Machine Learning algorithm benchmarking tool. The contents here will provide a perception to Machine Learning and will help beginners get familiar with the field quite a lot. Machine Learning techniques like Regression which further includes Linear and Logistic Regression, forming the basics of Neural Networks using familiar terms from Logistic regression would be mentioned here. Along with using TensorFlow, a Google’s project initiative which is widely used today for computational efficiency would be all of the techniques used here to solve the trivial game Cart-Pole

scholarly journals Validation of a machine learning algorithm for early severe sepsis prediction: a retrospective study predicting severe sepsis up to 48 h in advance using a diverse dataset from 461 US hospitals

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Hoyt Burdick ◽  
Eduardo Pino ◽  
Denise Gabel-Comeau ◽  
Carol Gu ◽  
Jonathan Roberts ◽  
...  
Keyword(s):  
Machine Learning ◽  
Severe Sepsis ◽  
Electronic Health Records ◽  
Patient Outcomes ◽  
Learning Algorithm ◽  
Validation Dataset ◽  
Machine Learning Algorithm ◽  
Health Records ◽  
Testing Dataset ◽  
Electronic Health

Abstract Background Severe sepsis and septic shock are among the leading causes of death in the United States and sepsis remains one of the most expensive conditions to diagnose and treat. Accurate early diagnosis and treatment can reduce the risk of adverse patient outcomes, but the efficacy of traditional rule-based screening methods is limited. The purpose of this study was to develop and validate a machine learning algorithm (MLA) for severe sepsis prediction up to 48 h before onset using a diverse patient dataset. Methods Retrospective analysis was performed on datasets composed of de-identified electronic health records collected between 2001 and 2017, including 510,497 inpatient and emergency encounters from 461 health centers collected between 2001 and 2015, and 20,647 inpatient and emergency encounters collected in 2017 from a community hospital. MLA performance was compared to commonly used disease severity scoring systems and was evaluated at 0, 4, 6, 12, 24, and 48 h prior to severe sepsis onset. Results 270,438 patients were included in analysis. At time of onset, the MLA demonstrated an AUROC of 0.931 (95% CI 0.914, 0.948) and a diagnostic odds ratio (DOR) of 53.105 on a testing dataset, exceeding MEWS (0.725, P < .001; DOR 4.358), SOFA (0.716; P < .001; DOR 3.720), and SIRS (0.655; P < .001; DOR 3.290). For prediction 48 h prior to onset, the MLA achieved an AUROC of 0.827 (95% CI 0.806, 0.848) on a testing dataset. On an external validation dataset, the MLA achieved an AUROC of 0.948 (95% CI 0.942, 0.954) at the time of onset, and 0.752 at 48 h prior to onset. Conclusions The MLA accurately predicts severe sepsis onset up to 48 h in advance using only readily available vital signs extracted from the existing patient electronic health records. Relevant implications for clinical practice include improved patient outcomes from early severe sepsis detection and treatment.

Sign in / Sign up

Export Citation Format

Share Document