Diabetes Induced Factors Prediction Based on Various Improved Machine Learning Methods

2021 ◽  
Vol 16 ◽  
Author(s):  
Jun Wu ◽  
Guoping Yang ◽  
Lulu Qu ◽  
Nan Han
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Potential Risk ◽  
Class Imbalance ◽  
The Elderly ◽  
Organ Damage ◽  
Initial Diagnosis ◽  
Machine Learning Algorithms ◽  
Data Set ◽  
Potential Risk Factors

Background: with the increasing quality of life of people, people begin to have more time and energy to pay attention to their own health problems. Among them, diabetes, as one of the most common and fastest-growing diseases, has attracted widespread attention from experts in bioinformatics. People of different ages all over the world suffer from diabetes which can shorten the life span of patients. Diabetes has a significant impact on human health, so that the accuracy of the initial diagnosis becomes essential. Diabetes can bring some serious complications, especially in the elderly, such as cardiovascular and cerebrovascular diseases, stroke, and multiple organ damage. The initial diagnosis of diabetes can reduce the possibility of deterioration. Identifying and analyzing potential risk factors for different physical attributes can help diagnose the prevalence of diabetes. The more accurate the prevalence, the more likely it is to reduce the incidence of complications. Methods: In this paper, we use the open source NHANES data set to analyze and determine potential risk factors relevant to diabetes by an improved version of Logistic Regression, SVM, and other improved machine learning algorithms. Results: Experimental results show that the improved version of Random Forest has the best effect, with a classification accuracy of 92%, and it can be found that age, blood-related diabetes, high blood pressure, cholesterol and BMI are the most important risk factors related to diabetes. Conclusion: Through the proposed method of machine learning, we can cope with the class imbalance and outlier detection problems.

scholarly journals Pressure Ulcers Prevalence and Potential Risk Factors Among Intensive Care Unit Patients in Governmental Hospitals in Palestine: A Cross-sectional Study

2019 ◽  
Vol 12 (1) ◽  
pp. 121-126 ◽  
Author(s):  
Jamal A.S. Qaddumi ◽  
Omar Almahmoud
Keyword(s):  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Potential Risk ◽  
Pressure Ulcers ◽  
Advisory Panel ◽  
Cross Sectional ◽  
Data Set ◽  
Potential Risk Factors ◽  
The Government

Aim: To determine the prevalence rate and the potential risk factors of pressure ulcers (PUs) among patients in the intensive care unit (ICU) departments of the government hospitals in Palestine. Methods: A quantitative, cross-sectional, descriptive analytical study was carried out in five government hospital intensive care units in four different Palestinian cities between September 27, 2017, and October 27, 2017. The data of 109 out of 115 (94.78%) inpatients were analyzed. The Minimum Data Set (MDS) recommended by the European Pressure Ulcer Advisory Panel (EPUAP) was used to collect inpatients’ information. Results: The result of the analysis showed that the prevalence of pressure ulcers in the ICU departments was 33%, and the prevalence of PUs when excluding stage one was 7.3%. The common stage for pressure ulcers was stage one. It was also determined that the most common risk factors for the development of pressure ulcers were the number of days in the hospital, moisture, and friction. Conclusion: According to the recent studies in the Asian States, the prevalence of pressure ulcers in Palestine is considerably higher than in China and Jordan. However, it is still lower than the prevalence reported in comparable published studies in Western Europe. Increasing the staff’s knowledge about PUs screening and preventive measures is highly recommended in order to decrease the burden of PUs.

scholarly journals A comprehensive evaluation of early potential risk factors for disease aggravation in patients with COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Qiang Tang ◽  
Yanwei Liu ◽  
Yingfeng Fu ◽  
Ziyang Di ◽  
Kailiang Xu ◽  
...  
Keyword(s):  
Risk Factors ◽  
Regression Analysis ◽  
Serum Ferritin ◽  
Potential Risk ◽  
Comprehensive Evaluation ◽  
Organ Damage ◽  
Kaplan Meier ◽  
Public Crisis ◽  
Potential Risk Factors ◽  
Multivariable Regression

AbstractThe 2019 Coronavirus Disease (COVID-19) has become an unprecedented public crisis. We retrospectively investigated the clinical data of 197 COVID-19 patients and identified 88 patients as disease aggravation cases. Compared with patients without disease aggravation, the aggravation cases had more comorbidities, including hypertension (25.9%) and diabetes (20.8%), and presented with dyspnoea (23.4%), neutrophilia (31.5%), and lymphocytopenia (46.7%). These patients were more prone to develop organ damage in liver, kidney, and heart (P < 0.05). A multivariable regression analysis showed that advanced age, comorbidities, dyspnea, lymphopenia, and elevated levels of Fbg, CTnI, IL-6, and serum ferritin were significant predictors of disease aggravation. Further, we performed a Kaplan–Meier analysis to evaluate the prognosis of COVID-19 patients, which suggested that 64.9% of the patients had not experienced ICU transfers and survival from the hospital.

scholarly journals Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation

10.2196/23128 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e23128
Author(s):  
Pan Pan ◽  
Yichao Li ◽  
Yongjiu Xiao ◽  
Bingchao Han ◽  
Longxiang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Data Set ◽  
Icu Patients ◽  
Potential Risk Factors

Background Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. Objective The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. Methods In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. Results Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. Conclusions The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

Effects of Class Imbalance Using Machine Learning Algorithms

2021 ◽  
Vol 12 (1) ◽  
pp. 1-17
Author(s):  
Swati V. Narwane ◽  
Sudhir D. Sawarkar
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Class Imbalance ◽  
Imbalanced Data ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Model Accuracy ◽  
Data Set ◽  
Class Distribution ◽  
Imbalance Problem

Class imbalance is the major hurdle for machine learning-based systems. Data set is the backbone of machine learning and must be studied to handle the class imbalance. The purpose of this paper is to investigate the effect of class imbalance on the data sets. The proposed methodology determines the model accuracy for class distribution. To find possible solutions, the behaviour of an imbalanced data set was investigated. The study considers two case studies with data set divided balanced to unbalanced class distribution. Testing of the data set with trained and test data was carried out for standard machine learning algorithms. Model accuracy for class distribution was measured with the training data set. Further, the built model was tested with individual binary class. Results show that, for the improvement of the system performance, it is essential to work on class imbalance problems. The study concludes that the system produces biased results due to the majority class. In the future, the multiclass imbalance problem can be studied using advanced algorithms.

scholarly journals A machine learning approach to investigate potential risk factors for gastroschisis in California

2018 ◽  
Vol 111 (4) ◽  
pp. 212-221 ◽  
Author(s):  
Kari A. Weber ◽  
Wei Yang ◽  
Suzan L. Carmichael ◽  
Amy M. Padula ◽  
Gary M. Shaw
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Potential Risk ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Potential Risk Factors

scholarly journals A Modified Drake Equation for Assessing Adversarial Risk to Machine Learning Models

2021 ◽  
Author(s):  
Josh Kalin ◽  
David Noever ◽  
Matthew Ciolino ◽  
Gerry Dozier
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Potential Risk ◽  
Learning Models ◽  
Contributing Factors ◽  
Empirical Measures ◽  
Research Fields ◽  
Potential Risk Factors ◽  
Adversarial Attack ◽  
Machine Learning Models

Machine learning models present a risk of adversarial attack when deployed in production. Quantifying the contributing factors and uncertainties using empirical measures could assist the industry with assessing the risk of downloading and deploying common model types. This work proposes modifying the traditional Drake Equation’s formalism to estimate the number of potentially successful adversarial attacks on a deployed model. The Drake Equation is famously used for parameterizing uncertainties and it has been used in many research fields outside of its original intentions to estimate the number of radio-capable extra-terrestrial civilizations. While previous work has outlined methods for discovering vulnerabilities in public model architectures, the proposed equation seeks to provide a semi-quantitative benchmark for evaluating and estimating the potential risk factors for adversarial attacks.

Predicting risk of stroke from lab tests using machine learning algorithms (Preprint)

2020 ◽  
Author(s):  
Eman Alanazi ◽  
Alaa Abdou ◽  
Jake Luo
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Prediction Models ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Health Records ◽  
Time Prediction ◽  
Health And Nutrition ◽  
Different Types ◽  
Potential Risk Factors

UNSTRUCTURED Stroke, a cerebrovascular disease, is one of the major causes of death. It is also causing a health burden for both the patients and the healthcare systems. One of the important risk factors of stroke is health behavior which is an increasing focus of prevention. In addition, chronic diseases such as hypertension, diabetes, cardiac diseases, and asthma are potential risk factors for stroke. There are a lot of machine learning that built using predictors such as lifestyle or radiology imaging. However, there are no models built using lab tests. The aim of the study is to fill this gap by building prediction models to predict stroke from lab tests. We utilized the National Health and Nutrition Examination Survey (NHNES) data sets to develop models that would predict stroke from patient lab tests. We found that accurate and sensitive machine learning models can be created to predict stroke from lab tests. The results showed that prediction with the best tested algorithm random forest could reach the highest accuracy (ACC = 0.96) when all the attributes were used. The model proposed can be integrated with electronic health records to provide a real-time prediction of stroke from lab tests. Due to the data, we could not predict the type of stroke wither hemorrigic or ischemic. In future studies, we aim to use data that provide different types of stroke and explore the data to build a prediction model of each type.

Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation (Preprint)

2020 ◽  
Author(s):  
Pan Pan ◽  
Yichao Li ◽  
Yongjiu Xiao ◽  
Bingchao Han ◽  
Longxiang Su ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Prediction Model ◽  
Risk Prediction ◽  
Risk Prediction Model ◽  
Data Set ◽  
Icu Patients ◽  
Potential Risk Factors

BACKGROUND Patients with COVID-19 in the intensive care unit (ICU) have a high mortality rate, and methods to assess patients’ prognosis early and administer precise treatment are of great significance. OBJECTIVE The aim of this study was to use machine learning to construct a model for the analysis of risk factors and prediction of mortality among ICU patients with COVID-19. METHODS In this study, 123 patients with COVID-19 in the ICU of Vulcan Hill Hospital were retrospectively selected from the database, and the data were randomly divided into a training data set (n=98) and test data set (n=25) with a 4:1 ratio. Significance tests, correlation analysis, and factor analysis were used to screen 100 potential risk factors individually. Conventional logistic regression methods and four machine learning algorithms were used to construct the risk prediction model for the prognosis of patients with COVID-19 in the ICU. The performance of these machine learning models was measured by the area under the receiver operating characteristic curve (AUC). Interpretation and evaluation of the risk prediction model were performed using calibration curves, SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), etc, to ensure its stability and reliability. The outcome was based on the ICU deaths recorded from the database. RESULTS Layer-by-layer screening of 100 potential risk factors finally revealed 8 important risk factors that were included in the risk prediction model: lymphocyte percentage, prothrombin time, lactate dehydrogenase, total bilirubin, eosinophil percentage, creatinine, neutrophil percentage, and albumin level. Finally, an eXtreme Gradient Boosting (XGBoost) model established with the 8 important risk factors showed the best recognition ability in the training set of 5-fold cross validation (AUC=0.86) and the verification queue (AUC=0.92). The calibration curve showed that the risk predicted by the model was in good agreement with the actual risk. In addition, using the SHAP and LIME algorithms, feature interpretation and sample prediction interpretation algorithms of the XGBoost black box model were implemented. Additionally, the model was translated into a web-based risk calculator that is freely available for public usage. CONCLUSIONS The 8-factor XGBoost model predicts risk of death in ICU patients with COVID-19 well; it initially demonstrates stability and can be used effectively to predict COVID-19 prognosis in ICU patients.

scholarly journals Lessons from History: Association of Porphyromonas Gingivalis (a Major Periodontopathic Bacteria) in Alzheimer’s Disease Patients

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Joao Carnio DDS ◽  
Anna Tereza Carnio
Keyword(s):  
Risk Factors ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Potential Risk ◽  
Health Problem ◽  
Treatment Options ◽  
The Elderly ◽  
Preventive Dental Care ◽  
Potential Risk Factors

Alzheimer’s disease (AD), a fatal neurodegenerative condition that affects the elderly, is a serious health problem for geriatric subjects worldwide. AD incidence increases significantly with age. It is almost 50% common in 85 -yearolds. [1] AD prevalence will rise as the population grows older and lives spans increase. It is estimated that around 14 million people will be affected by AD in the next 50 years. Switching to newer treatments can help reduce the incidence of AD. These treatment options can be effective against potential risk factors and delay the onset. What is the role of periodontitis in Alzheimer’s disease? This work aims to do a systematic, integrative review on published literature to evaluate if there is a link between Porphyromonas gumivalis (P. gingivalis), and Alzheimer’s. Part of (?) Part of (?) P. gingivalis could serve as a therapeutic target for patients suffering from Alzheimer’s disease. It also help s to reduce the severity and incidence of the condition. Patients with Alzheimer’s disease could benefit from preventive dental care and the inhibition of neurotoxicity by P. gingivalis.

Premature Arterial Disease Associated with Familial Antithrombin III Deficiency

1990 ◽  
Vol 63 (01) ◽  
pp. 013-015 ◽  
Author(s):  
E J Johnson ◽  
C R M Prentice ◽  
L A Parapia
Keyword(s):  
Risk Factors ◽  
Venous Thromboembolism ◽  
Potential Risk ◽  
Antithrombin Iii ◽  
Arterial Disease ◽  
Coagulation System ◽  
Early Age ◽  
Arterial Thromboembolism ◽  
Potential Risk Factors ◽  
Antithrombin Iii Deficiency

SummaryAntithrombin III (ATIII) deficiency is one of the few known abnormalities of the coagulation system known to predispose to venous thromboembolism but its relation to arterial disease is not established. We describe two related patients with this disorder, both of whom suffered arterial thrombotic events, at an early age. Both patients had other potential risk factors, though these would normally be considered unlikely to lead to such catastrophic events at such an age. Thrombosis due to ATIII deficiency is potentially preventable, and this diagnosis should be sought more frequently in patients with arterial thromboembolism, particularly if occurring at a young age. In addition, in patients with known ATIII deficiency, other risk factors for arterial disease should be eliminated, if possible. In particular, these patients should be counselled against smoking.

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