scholarly journals Predicting Increased Blood Pressure Using Machine Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Hudson Fernandes Golino ◽  
Liliany Souza de Brito Amaral ◽  
Stenio Fernando Pimentel Duarte ◽  
Cristiano Mauro Assis Gomes ◽  
Telma de Jesus Soares ◽  
...  
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Predictive Power ◽  
Classification Tree ◽  
Training Group ◽  
Test Sample ◽  
Hip Circumference ◽  
Training Set ◽  
Learning Technique ◽  
Tree Data

The present study investigates the prediction of increased blood pressure by body mass index (BMI), waist (WC) and hip circumference (HC), and waist hip ratio (WHR) using a machine learning technique named classification tree. Data were collected from 400 college students (56.3% women) from 16 to 63 years old. Fifteen trees were calculated in the training group for each sex, using different numbers and combinations of predictors. The result shows that for women BMI, WC, and WHR are the combination that produces the best prediction, since it has the lowest deviance (87.42), misclassification (.19), and the higher pseudoR2(.43). This model presented a sensitivity of 80.86% and specificity of 81.22% in the training set and, respectively, 45.65% and 65.15% in the test sample. For men BMI, WC, HC, and WHC showed the best prediction with the lowest deviance (57.25), misclassification (.16), and the higher pseudoR2(.46). This model had a sensitivity of 72% and specificity of 86.25% in the training set and, respectively, 58.38% and 69.70% in the test set. Finally, the result from the classification tree analysis was compared with traditional logistic regression, indicating that the former outperformed the latter in terms of predictive power.

scholarly journals Exploratory analysis on prediction of loan privilege for customers using random forest

2018 ◽  
Vol 7 (2.21) ◽  
pp. 339 ◽  
Author(s):  
K Ulaga Priya ◽  
S Pushpa ◽  
K Kalaivani ◽  
A Sartiha
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Data Model ◽  
Model Evaluation ◽  
Banking Industry ◽  
Performance Parameters ◽  
Training Set ◽  
Test Set ◽  
Learning Technique ◽  
Analytical Processing

In Banking Industry loan Processing is a tedious task in identifying the default customers. Manual prediction of default customers might turn into a bad loan in future. Banks possess huge volume of behavioral data from which they are unable to make a judgement about prediction of loan defaulters. Modern techniques like Machine Learning will help to do analytical processing using Supervised Learning and Unsupervised Learning Technique. A data model for predicting default customers using Random forest Technique has been proposed. Data model Evaluation is done on training set and based on the performance parameters final prediction is done on the Test set. This is an evident that Random Forest technique will help the bank to predict the loan Defaulters with utmost accuracy.  

scholarly journals An improved catalogue of putative synaptic genes defined by their temporal transcription profiles through an ensemble machine learning approach

2019 ◽  
Author(s):  
Flavio Pazos ◽  
Pablo Soto ◽  
Martín Palazzo ◽  
Gustavo Guerberoff ◽  
Patricio Yankilevich ◽  
...  
Keyword(s):  
Machine Learning ◽  
Empirical Data ◽  
Predictive Power ◽  
Learning Model ◽  
Synaptic Function ◽  
Training Set ◽  
Training Scheme ◽  
Transcription Profiles ◽  
Ensemble Machine Learning ◽  
Machine Learning Model

Abstract Background. Assembly and function of neuronal synapses require the coordinated expression of a yet undetermined set of genes. Previously, we had trained an ensemble machine learning model to assign a probability of having synaptic function to every protein-coding gene in Drosophila melanogaster. This approach resulted in the publication of a catalogue of 893 genes that was postulated to be very enriched in genes with still undocumented synaptic functions. Since then, the scientific community has experimentally identified 79 new synaptic genes. Here we used these new empirical data to evaluate the predictive power of the catalogue. Then we implemented a series of improvements to the training scheme and the ensemble rules of our model and added the new synaptic genes to the training set, to obtain a new, enhanced catalogue of putative synaptic genes. Results. The retrospective analysis demonstrated that our original catalogue was indeed highly enriched in genes with unknown synaptic function. The changes to the training scheme and the ensemble rules resulted in a catalogue with better predictive power. Finally, training this improved model with an updated training set, that includes all the new synaptic genes, we obtained a new, enhanced catalogue of putative synaptic genes, which we present here announcing a regularly updated version that will be available online at: http://synapticgenes.bnd.edu.uy Conclusions. We show that training a machine learning model solely with the whole-body temporal transcription profiles of known synaptic genes resulted in a catalogue with a significant enrichment in undiscovered synaptic genes. Using new empirical data, we validated our original approach, improved our model an obtained a better catalogue. The utility of this approach is that it reduces the number of genes to be tested through hypothesis-driven experimentation.

Abstract 349: Prognostication for Out-of-Hospital Cardiogenic Cardiac Arrest Patients Using Advanced Machine Learning Technique

Circulation ◽  
2018 ◽  
Vol 138 (Suppl_2) ◽  
Author(s):  
Tomohisa Seki ◽  
Tomoyoshi Tamura ◽  
Masaru Suzuki
Keyword(s):  
Machine Learning ◽  
Cardiac Arrest ◽  
Study Data ◽  
Machine Learning Techniques ◽  
Operating Characteristics ◽  
Training Set ◽  
Machine Learning Technique ◽  
Test Set ◽  
Medical Institutions ◽  
Learning Technique

Introduction and Objective: Early prognostication for cardiogenic out-of-hospital cardiac arrest (OHCA) patients remain challenging. Recently, advanced machine learning techniques have been employed for clinical diagnosis and prognostication for various conditions. Therefore, in this study, we attempted to establish a prognostication model for cardiogenic OHCA using an advanced machine learning technique. Methods and Results: Data of a prospective multi-center cohort study of OHCA patients transported by an ambulance to 67 medical institutions in Kanto area of Japan between January 2012 and March 2013 was used in this study. Data for cardiogenic OHCA patients aged ≥18 years were retrieved and patients were grouped according to the time of calls for ambulances (training set: between January 1, 2012 and December 12, 2012; test set: between January 1, 2013 and March 31, 2013). From among 421 variables observed during the period between calls for ambulances and initial in-hospital treatments of cardiogenic OHCA, 38 prehospital factors or 56 prehospital factors and initial in-hospital factors were used for prognostication, respectively. Prognostication models for 1-year survival were established with random forest method, an advanced machine learning method that aggregates a series of decision trees for classification and regression. After 10-fold internal cross validation in the training set, prognostication models were validated using test set. Area under the receiver operating characteristics curve (AUC) was used to evaluate the prediction performance of models. Prognostication models trained with 38 variables or 56 variables for 1-year survival showed AUC values of 0.93±0.01 and 0.95±0.01, respectively. Conclusions: Prognostication models trained with advanced machine learning technique showed favorable prediction capability for 1-year survival of cardiogenic OHCA. These results indicate that an advanced machine learning technique can be applicable to establish early prognostication model for cardiogenic OHCA.

scholarly journals SELECTING KDD FEATURES AND USING RANDOM CLASSIFICATION TREE FOR PREDICTING ATTACKS

2014 ◽  
pp. 115-123
Author(s):  
Rachid Beghdad
Keyword(s):  
Machine Learning ◽  
Classification Tree ◽  
Regression Tree ◽  
Classification Trees ◽  
Classification And Regression Tree ◽  
Data Sets ◽  
Machine Learning Technique ◽  
Learning Technique ◽  
Classification And Regression ◽  
Anova Technique

The purpose of this study is to identify some higher-level KDD features, and to train the resulting set with an appropriate machine learning technique, in order to classify and predict attacks. To achieve that, a two-steps approach is proposed. Firstly, the Fisher’s ANOVA technique was used to deduce the important features. Secondly, 4 types of classification trees: ID3, C4.5, classification and regression tree (CART), and random tree (RnDT), were tested to classify and detect attacks. According to our tests, the RndT leads to the better results. That is why we will present here the classification and prediction results of this technique in details. Some of the remaining results will be used later to make comparisons. We used the KDD’99 data sets to evaluate the considered algorithms. For these evaluations, only the four attack categories’ case was considered. Our simulations show the efficiency of our approach, and show also that it is very competitive with some similar previous works.

scholarly journals A machine learning approach to identify correlates of current e-cigarette use in Canada

2021 ◽  
Author(s):  
Rui Fu ◽  
Nicholas Mitsakakis ◽  
Michael Chaiton
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Electronic Cigarettes ◽  
Classification Tree ◽  
Cigarette Use ◽  
Training Set ◽  
Tree Model ◽  
Cross Sectional ◽  
Classification Tree Model ◽  
Validation Set

Aim: Popularity of electronic cigarettes (i.e. e-cigarettes) is soaring in Canada. Understanding person-level correlates of current e-cigarette use (vaping) is crucial to guide tobacco policy, but prior studies have not fully identified these correlates due to model overfitting caused by multicollinearity. This study addressed this issue by using classification tree, a machine learning algorithm. Methods: This population-based cross-sectional study used the Canadian Tobacco, Alcohol, and Drugs Survey (CTADS) from 2017 that targeted residents aged 15 or older. Forty-six person-level characteristics were first screened in a logistic mixed-effects regression procedure for their strength in predicting vaper type (current vs. former vaper) among people who reported to have ever vaped. A 9:1 ratio was used to randomly split the data into a training set and a validation set. A classification tree model was developed using the cross-validation method on the training set using the selected predictors and assessed on the validation set using sensitivity, specificity and accuracy. Results: Of the 3,059 people with an experience of vaping, the average age was 24.4 years (standard deviation = 11.0), with 41.9% of them being female and 8.5% of them being aboriginal. There were 556 (18.2%) current vapers. The classification tree model performed relatively well and suggested attraction to e-cigarette flavors was the most important correlate of current vaping, followed by young age (< 18) and believing vaping to be less harmful to oneself than cigarette smoking. Conclusions: People who vape due to flavors are associated with very high risk of becoming current vapers. The findings of this study provide evidence that supports the ongoing ban on flavored vaping products in the US and suggests a similar regulatory intervention may be effective in Canada.

scholarly journals Classification of Categorical Outcome VariablenBased on Logistic Regression and Tree Algorithm

2020 ◽  
Vol 8 (5) ◽  
pp. 4685-4690
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Decision Tree ◽  
Classification Tree ◽  
Research Work ◽  
Large Data ◽  
Training Set ◽  
Tree Algorithm ◽  
Machine Learning Classification

Logistic regression is most popular techniques incorporated in traditional statistics. Usually, this regression is applicable when the dependent variable is of categorical binary in nature. In the field of Statistics and Machine learning, classification of data is critical to discriminate to which set of clusters a new observation belongs, in the base of training set of a data containing observation whose group relationship is known. In this paper, we are focusing on the concepts of Logistic regression and classification tree. A large data taken from UCI (Machine learning Repository) incorporated for this research work. The aim of study is to distinguish the results obtained from Logistic regression and decision tree. At the end, decision tree gives better results than Logistic regression.

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

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