On Patient’s Characteristics Extraction for Metabolic Syndrome Diagnosis: Predictive Modelling Based on Machine Learning

2014 ◽  
pp. 118-132 ◽  
Author(s):  
František Babič ◽  
Ljiljana Majnarić ◽  
Alexandra Lukáčová ◽  
Ján Paralič ◽  
Andreas Holzinger
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Predictive Modelling ◽  
Syndrome Diagnosis

Abstract #134: Metabolic Syndrome Diagnosis and Management in Continuity Care

2004 ◽  
Vol 10 ◽  
pp. 46-47
Author(s):  
S. Dipp ◽  
P. Charney ◽  
N. Rennert
Keyword(s):  
Metabolic Syndrome ◽  
Diagnosis And Management ◽  
Syndrome Diagnosis

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

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
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.

scholarly journals Impact of self-reported fasting duration on lipid profile variability, cardiovascular risk stratification and metabolic syndrome diagnosis

2018 ◽  
Author(s):  
Carolina Castro Porto Silva Janovsky ◽  
Antonio Laurinavicius ◽  
Fernando Cesena ◽  
Viviane Valente ◽  
Carlos Eduardo Ferreira ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Cardiovascular Risk ◽  
Risk Stratification ◽  
Lipid Profile ◽  
Cardiovascular Risk Stratification ◽  
Syndrome Diagnosis ◽  
Fasting Duration

scholarly journals Rethinking multiscale cardiac electrophysiology with machine learning and predictive modelling

2019 ◽  
Vol 104 ◽  
pp. 339-351 ◽  
Author(s):  
Chris D. Cantwell ◽  
Yumnah Mohamied ◽  
Konstantinos N. Tzortzis ◽  
Stef Garasto ◽  
Charles Houston ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiac Electrophysiology ◽  
Predictive Modelling

scholarly journals Systematic Methods on Machine Learning Techniques for Clinical Predictive Modelling

2020 ◽  
Vol 9 (5) ◽  
pp. 288-297
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
High Performance ◽  
Predictive Modelling ◽  
Medical Application ◽  
Medical Data ◽  
Machine Learning Techniques ◽  
Screening Process ◽  
Huge Data ◽  
System Data

Predictive modelling is a mathematical technique which uses Statistics for prediction, due to the rapid growth of data over the cloud system, data mining plays a significant role. Here, the term data mining is a way of extracting knowledge from huge data sources where it’s increasing the attention in the field of medical application. Specifically, to analyse and extract the knowledge from both known and unknown patterns for effective medical diagnosis, treatment, management, prognosis, monitoring and screening process. But the historical medical data might include noisy, missing, inconsistent, imbalanced and high dimensional data.. This kind of data inconvenience lead to severe bias in predictive modelling and decreased the data mining approach performances. The various pre-processing and machine learning methods and models such as Supervised Learning, Unsupervised Learning and Reinforcement Learning in recent literature has been proposed. Hence the present research focuses on review and analyses the various model, algorithm and machine learning technique for clinical predictive modelling to obtain high performance results from numerous medical data which relates to the patients of multiple diseases.

scholarly journals Predicting Metabolic Syndrome With Machine Learning Models Using a Decision Tree Algorithm: Retrospective Cohort Study (Preprint)

2019 ◽  
Author(s):  
Cheng-Sheng Yu ◽  
Yu-Jiun Lin ◽  
Chang-Hsien Lin ◽  
Sen-Te Wang ◽  
Shiyng-Yu Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Logistic Regression ◽  
Decision Tree ◽  
Characteristic Curve ◽  
Significant Risk ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Health Examination ◽  
Multivariate Logistic Regression

BACKGROUND Metabolic syndrome is a cluster of disorders that significantly influence the development and deterioration of numerous diseases. FibroScan is an ultrasound device that was recently shown to predict metabolic syndrome with moderate accuracy. However, previous research regarding prediction of metabolic syndrome in subjects examined with FibroScan has been mainly based on conventional statistical models. Alternatively, machine learning, whereby a computer algorithm learns from prior experience, has better predictive performance over conventional statistical modeling. OBJECTIVE We aimed to evaluate the accuracy of different decision tree machine learning algorithms to predict the state of metabolic syndrome in self-paid health examination subjects who were examined with FibroScan. METHODS Multivariate logistic regression was conducted for every known risk factor of metabolic syndrome. Principal components analysis was used to visualize the distribution of metabolic syndrome patients. We further applied various statistical machine learning techniques to visualize and investigate the pattern and relationship between metabolic syndrome and several risk variables. RESULTS Obesity, serum glutamic-oxalocetic transaminase, serum glutamic pyruvic transaminase, controlled attenuation parameter score, and glycated hemoglobin emerged as significant risk factors in multivariate logistic regression. The area under the receiver operating characteristic curve values for classification and regression trees and for the random forest were 0.831 and 0.904, respectively. CONCLUSIONS Machine learning technology facilitates the identification of metabolic syndrome in self-paid health examination subjects with high accuracy.

scholarly journals Predictive modelling of turbofan engine components condition using machine and deep learning methods

2021 ◽  
Vol 23 (2) ◽  
pp. 359-370
Author(s):  
Michał Matuszczak ◽  
Mateusz Żbikowski ◽  
Andrzej Teodorczyk
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Predictive Modelling ◽  
Continuous Variable ◽  
Environmental Data ◽  
Bayesian Optimization ◽  
Aviation Industry ◽  
Learning Models ◽  
Turbofan Engine ◽  
Machine Learning Models

The article proposes an approach based on deep and machine learning models to predict a component failure as an enhancement of condition based maintenance scheme of a turbofan engine and reviews currently used prognostics approaches in the aviation industry. Component degradation scale representing its life consumption is proposed and such collected condition data are combined with engines sensors and environmental data. With use of data manipulation techniques, a framework for models training is created and models' hyperparameters obtained through Bayesian optimization. Models predict the continuous variable representing condition based on the input. Best performed model is identified by detemining its score on the holdout set. Deep learning models achieved 0.71 MSE score (ensemble meta-model of neural networks) and outperformed significantly machine learning models with their best score at 1.75. The deep learning models shown their feasibility to predict the component condition within less than 1 unit of the error in the rank scale.

Predictive modelling of hospital readmission: Evaluation of different preprocessing techniques on machine learning classifiers

2021 ◽  
Vol 25 (5) ◽  
pp. 1073-1098
Author(s):  
Nor Hamizah Miswan ◽  
Chee Seng Chan ◽  
Chong Guan Ng
Keyword(s):  
Machine Learning ◽  
Hospital Readmission ◽  
Performance Metrics ◽  
Predictive Performance ◽  
Predictive Modelling ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
Set Up ◽  
The Right ◽  
The Impact

Hospital readmission is a major cost for healthcare systems worldwide. If patients with a higher potential of readmission could be identified at the start, existing resources could be used more efficiently, and appropriate plans could be implemented to reduce the risk of readmission. Therefore, it is important to predict the right target patients. Medical data is usually noisy, incomplete, and inconsistent. Hence, before developing a prediction model, it is crucial to efficiently set up the predictive model so that improved predictive performance is achieved. The current study aims to analyse the impact of different preprocessing methods on the performance of different machine learning classifiers. The preprocessing applied by previous hospital readmission studies were compared, and the most common approaches highlighted such as missing value imputation, feature selection, data balancing, and feature scaling. The hyperparameters were selected using Bayesian optimisation. The different preprocessing pipelines were assessed using various performance metrics and computational costs. The results indicated that the preprocessing approaches helped improve the model’s prediction of hospital readmission.

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