Predictive model for acute myocardial infarction in working-age population: a machine learning approach

Cardiovascular diseases are the leading cause of mortality in Latin America, particularly acute myocardial infarction (AMI), which is the primary cause of atherosclerotic cardiovascular morbidity. This study aims to develop a predictive model for the probability of AMI occurrence in the working-age population, based on atherogenic indices, paraclinical variables, and anthropometric measures. The research conducted a cross-sectional study involving 427 workers aged 40 years or older in Popayán, Colombia. Out of this population, 202 individuals were screened with a 95% confidence interval and a 5% error margin. Epidemiological, anthropometric, and paraclinical data were collected. A binary logistic regression model was employed to identify variables directly associated with the probability of AMI. Predictive classification models were generated using statistical software JASP and the programming language Python. During the training stage, JASP produced a model with an accuracy of 87.5%, while Python generated a model with an accuracy of 90.2%. In the validation stage, JASP achieved an accuracy of 93%, and Python reached 95%. These results establish an effective model for predicting the probability of AMI in the working population

Framework to predict the metabolic syndrome without doing a blood test: based on machine learning for a clinical decision support system

Metabolic Syndrome (MetS) is a cluster of risk factors that increase the likelihood of heart disease and diabetes mellitus, and researchers have recently linked it to worse outcomes for the novel Covid-19 disease. It is crucial to get diagnosed with time to take preventive measures, especially for patients in locations without proper laboratories and medical consultations. This work presents a new model to diagnose metabolic syndrome using machine learning and non-biochemical variables that healthcare professionals can obtain from initial consultations. For evaluating and comparing the model, this work also proposes a new methodology for performing research on data mining called RAMAD. The methodology standardizes the novel model’s comparison with similar classification models, using their reported variables and previously obtained data from a study in Colombia, using the holdout and random subsampling validation methods to get performance evaluation indicators between the models. The resulting ANN model used three hidden layers and only Hip Circumference, dichotomous Waist Circumference, and dichotomous blood pressure variables. It gave an Area under Receiver Operating Characteristic curves (AROC) of 87.75% by the International Diabetes Federation (IDF) and 85.12% by Harmonized Diagnosis or Joint Interim Statement (HMS) diagnosis criteria, higher than previous models. Thanks to the new methodology, diagnosis models can be thoroughly documented for appropriate future comparisons, thus benefiting the studied diseases’ diagnosis. Medical personnel needs to know the factors involved in the syndrome to start a treatment. So, this work also presents the segmentation of metabolic syndrome in types related to each biochemical variable. It uses the RAMAD methodology together with several machine learning techniques to design a framework to predict MetS and their several types, without using a blood test and only anthropometric and clinical information. The results showed an excellent system for predicting six MetS types that combine several factors mentioned above that have an AROC with a range of 71% to 96%, and an AROC 82.86%. This thesis finishes with the proposal of using a SCRUM Thinking framework for creating mobile health applications to implement the new models and serve as decision tools for healthcare professionals. The standard and fundamental characteristics were analyzed, finding the quality attributes verified in the framework’s early stages. Keywords — Metabolic Syndrome, Segmentation, Quine–McCluskey, Random Subsampling validation, RAMAD, Machine learning, Framework, International Diabetes Federation (IDF), Harmonized Diagnosis or Joint Interim Statement (HMS).DoctoradoDoctor en Ingeniería de Sistemas y Computació

Development of the decision support system in treatment of arterial hypertension application of artificial neural networks for evaluation of heart rate variability signals

The article presents first step of the decision support system development in treatment of arterial hypertension. Results of arterial hypertension diagnostic task by short-term signals of heart rate variability are presented. The tilt test study was used as the functional load. The peculiarity of this work is application of neural networks for this task. The different number of hidden layers in the neural networks and different number of neurons in hidden layers were tested in this study. The classification accuracy of the neural networks was compared with those of simple machine learning classifiers. The following steps of the decision support system development are declared. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

Machine learning-based predictive model for prevention of metabolic syndrome

Metabolic syndrome (MetS) is a chronic disease caused by obesity, high blood pressure, high blood sugar, and dyslipidemia and may lead to cardiovascular disease or type 2 diabetes. Therefore, the detection and prevention of MetS at an early stage are imperative. Individuals can detect MetS early and manage it effectively if they can easily monitor their health status in their daily lives. In this study, a predictive model for MetS was developed utilizing solely noninvasive information, thereby facilitating its practical application in real-world scenarios. The model\u27s construction deliberately excluded three features requiring blood testing, specifically those for triglycerides, blood sugar, and HDL cholesterol. We used a large-scale Korean health examination dataset (n = 70, 370; the prevalence of MetS = 13.6%) to develop the predictive model. To obtain informative features, we developed three novel synthetic features from four basic information: waist circumference, systolic and diastolic blood pressure, and gender. We tested several classification algorithms and confirmed that the decision tree model is the most appropriate for the practical prediction of MetS. The proposed model achieved good performance, with an AUC of 0.889, a recall of 0.855, and a specificity of 0.773. It uses only four base features, which results in simplicity and easy interpretability of the model. In addition, we performed calibrations on the prediction probability and calibrated the model. Therefore, the proposed model can provide MetS diagnosis and risk prediction results. We also proposed a MetS risk map such that individuals could easily determine whether they had metabolic syndrome

Artificial neural networks model: Neuropsychological variables and their relationship with body fat percentage in adults

Background: There is a growing interest to understand the neural functions and substrates of complex cognitive processes related to Obesity (OB). Artificial Intelligence (AI) is being applied, specifically the perceptron model of Artificial Neural Networks (ANN) in non-communicable chronic diseases, to identify with greater certainty the connective factors (synaptic networks) between the input variables and the output variables associated. Objective: Identify the synaptic weights of the ANN whose input variables are the executive functions (EF) and healthy lifestyles as predictors of Body Fat Percentage (BFP) in a group of adult subjects with different levels of BFP. Methods: The Neuropsychological Battery (BANFE-2) and the Overeating Questionnaire (OQ) were administered to 40 participants aged between 18 and 38 years. BFP was measured using a RENPHO ES-24M Smart Body Composition Scale. The perceptron ANN model with ten trials was applied with a multilayer-perceptron. Results: The ANN showed that the sensory variables with greater synaptic weight for BFP were Stroop A and B Errors and Successes of BANFE-2, and OQ scales Rationalizations and Healthy Habits. Conclusions: ANN proved to be important in the simultaneous analysis of neuropsychological and healthy lifestyle data for the analysis of OB prevention and treatment by identifying the variables that are closely related

Kidney Ailment Prediction under Data Imbalance

Chronic Kidney Disease (CKD) is the leading cause for kidney failure. It is a global health problem affecting approximately 10% of the world population and about 15% of US adults. Chronic Kidney Diseases do not generally show any disease specific symptoms in early stages thus it is hard to detect and prevent such diseases. Early detection and classification are the key factors in managing Chronic Kidney Diseases. In this thesis, we propose a new machine learning technique for Kidney Ailment Prediction. We focus on two key issues in machine learning, especially in its application to disease prediction. One is related to class imbalance problem. This occurs when at least one of the classes are represented by significantly smaller number of samples than the others in the training set. The problem with imbalanced dataset is that the classifiers tend to classify all samples as majority class, ignoring the minority class samples. The second issue is on the specific type of data to be used for a given problem. Here, we focused on predicting kidney diseases based on patient information extracted from laboratory and questionnaire data. Most recent approaches for predicting kidney diseases or other chronic diseases rely on the usage of prescription drugs. In this study, we focus on biomarker and anthropometry data of patients to analyze and predict kidney-related diseases. In this research, we adopted a learning approach which involves repeated random data sub-sampling to tackle the class imbalance problem. This technique divides the samples into multiple sub-samples, while keeping each training sub-sample completely balanced. We then trained classification models on the balanced data to predict the risk of kidney failure. Further, we developed an intelligent fusion mechanism to combine information from both the biomarker and anthropometry data sets for improved prediction accuracy and stability. Results are included to demonstrate the performance

Utilizing Temporal Information in The EHR for Developing a Novel Continuous Prediction Model

Type 2 diabetes mellitus (T2DM) is a nation-wide prevalent chronic condition, which includes direct and indirect healthcare costs. T2DM, however, is a preventable chronic condition based on previous clinical research. Many prediction models were based on the risk factors identified by clinical trials. One of the major tasks of the T2DM prediction models is to estimate the risks for further testing by HbA1c or fasting plasma glucose to determine whether the patient has or does not have T2DM because nation-wide screening is not cost-effective. Those models had substantial limitations on data quality, such as missing values. In this dissertation, I tested the conventional models which were based on the most widely used risk factors to predict the possibility of developing T2DM. The AUC was an average of 0.5, which implies the conventional model cannot be used to screen for T2DM risks. Based on this result, I further implemented three types of temporal representations, including non-temporal representation, interval-temporal representation, and continuous-temporal representation for building the T2DM prediction model. According to the results, continuous-temporal representation had the best performance. Continuous-temporal representation was based on deep learning methods. The result implied that the deep learning method could overcome the data quality issue and could achieve better performance. This dissertation also contributes to a continuous risk output model based on the seq2seq model. This model can generate a monotonic increasing function for a given patient to predict the future probability of developing T2DM. The model is workable but still has many limitations to overcome. Finally, this dissertation demonstrates some risks factors which are underestimated and are worthy for further research to revise the current T2DM screening guideline. The results were still preliminary. I need to collaborate with an epidemiologist and other fields to verify the findings. In the future, the methods for building a T2DM prediction model can also be used for other prediction models of chronic conditions

Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study

BACKGROUND: Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA. METHODS: The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models. RESULTS: The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001). CONCLUSIONS: The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk.ope

Prediction of Concurrent Hypertensive Disorders in Pregnancy and Gestational Diabetes Mellitus Using Machine Learning Techniques

Gestational diabetes mellitus and hypertensive disorders in pregnancy are serious maternal health conditions with immediate and lifelong mother-child health consequences. These obstetric pathologies have been widely investigated, but mostly in silos, while studies focusing on their simultaneous occurrence rarely exist. This is especially the case in the machine learning domain. This retrospective study sought to investigate, construct, evaluate, compare, and isolate a supervised machine learning predictive model for the binary classification of co-occurring gestational diabetes mellitus and hypertensive disorders in pregnancy in a cohort of otherwise healthy pregnant women. To accomplish the stated aims, this study analyzed an extract (n=4624, n_features=38) of a labelled maternal perinatal dataset (n=9967, n_fields=79) collected by the PeriData.Net® database from a participating community hospital in Southeast Wisconsin between 2013 and 2018. The datasets were named, “WiseSample” and “WiseSubset” respectively in this study. Thirty-three models were constructed with the six supervised machine learning algorithms explored on the extracted dataset: logistic regression, random forest, decision tree, support vector machine, StackingClassifier, and KerasClassifier, which is a deep learning classification algorithm; all were evaluated using the StratifiedKfold cross-validation (k=10) method. The Synthetic Minority Oversampling Technique was applied to the training data to resolve the class imbalance that was noted in the sub-sample at the preprocessing phase. A wide range of evidence-based feature selection techniques were used to identify the best predictors of the comorbidity under investigation. Multiple model performance evaluation metrics that were employed to quantitatively evaluate and compare model performance quality include accuracy, F1, precision, recall, and the area under the receiver operating characteristic curve. Support Vector Machine objectively emerged as the most generalizable model for identifying the gravidae in WiseSubset who may develop concurrent gestational diabetes mellitus and hypertensive disorders in pregnancy, scoring 100.00% (mean) in recall. The model consisted of 9 predictors extracted by the recursive feature elimination with cross-validation with random forest. Finding from this study show that appropriate machine learning methods can reliably predict comorbid gestational diabetes and hypertensive disorders in pregnancy, using readily available routine prenatal attributes. Six of the nine most predictive factors of the comorbidity were also in the top 6 selections of at least one other feature selection method examined. The six predictors are healthy weight prepregnancy BMI, mother’s educational status, husband’s educational status, husband’s occupation in one year before the current pregnancy, mother’s blood group, and mother’s age range between 34 and 44 years. Insight from this analysis would support clinical decision making of obstetric experts when they are caring for 1.) nulliparous women, since they would have no obstetric history that could prompt their care providers for feto-maternal medical surveillance; and 2.) the experienced mothers with no obstetric history suggestive of any of the disease(s) under this study. Hence, among other benefits, the artificial-intelligence-backed tool designed in this research would likely improve maternal and child care quality outcomes