Accurate Prediction of Myocardial Infarction By Comparing Logistic Regression Algorithm with CatBoost Classifier

Aim: The forecast of Myocardial Infarction for humans employing a Machine learning model by corresponding a Logistic Regression Algorithm with a CatBoost Classifier. The accuracy is enhanced by utilizing the novel LR Classifier. Materials and Methods: The study utilized a total of 20 sample iterations, with 10 samples per group. Group 1 was analyzed using a logistic regression algorithm, while Group 2 was analyzed using a decision tree classifier. The statistical power was set at 80%, and the confidence level was set at 95%. Results: The accuracy of the outcome with logistic regression is 94.61% and CatBoost Classifier is 79.516%, both the groups are statistically significant as p = 0.015 (<0.05) is the significant value in the independent sample T-test between LR and CB Classifier. Conclusion: This research concludes that the logistic regression algorithm gives the most accurate mortality with the difference of 15.1%, compared to the CatBoost Classifier

Machine learning prediction of mortality in acute myocardial infarction

© The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.Background: Acute Myocardial Infarction (AMI) is the leading cause of death in Portugal and globally. The present investigation created a model based on machine learning for predictive analysis of mortality in patients with AMI upon admission, using different variables to analyse their impact on predictive models. Methods: Three experiments were built for mortality in AMI in a Portuguese hospital between 2013 and 2015 using various machine learning techniques. The three experiments differed in the number and type of variables used. We used a discharged patients' episodes database, including administrative data, laboratory data, and cardiac and physiologic test results, whose primary diagnosis was AMI. Results: Results show that for Experiment 1, Stochastic Gradient Descent was more suitable than the other classification models, with a classification accuracy of 80%, a recall of 77%, and a discriminatory capacity with an AUC of 79%. Adding new variables to the models increased AUC in Experiment 2 to 81% for the Support Vector Machine method. In Experiment 3, we obtained an AUC, in Stochastic Gradient Descent, of 88% and a recall of 80%. These results were obtained when applying feature selection and the SMOTE technique to overcome imbalanced data. Conclusions: Our results show that the introduction of new variables, namely laboratory data, impacts the performance of the methods, reinforcing the premise that no single approach is adapted to all situations regarding AMI mortality prediction. Instead, they must be selected, considering the context and the information available. Integrating Artificial Intelligence (AI) and machine learning with clinical decision-making can transform care, making clinical practice more efficient, faster, personalised, and effective. AI emerges as an alternative to traditional models since it has the potential to explore large amounts of information automatically and systematically.The present publication was funded by Fundação Ciência e Tecnologia, IP national support through CHRC (UIDP/04923/2020).info:eu-repo/semantics/publishedVersio

The Quality Application of Deep Learning in Clinical Outcome Predictions Using Electronic Health Record Data: A Systematic Review

Introduction: Electronic Health Record (EHR) is a significant source of medical data that can be used to develop predictive modelling with therapeutically useful outcomes. Predictive modelling using EHR data has been increasingly utilized in healthcare, achieving outstanding performance and improving healthcare outcomes. Objectives: The main goal of this review study is to examine different deep learning approaches and techniques used to EHR data processing. Methods: To find possibly pertinent articles that have used deep learning on EHR data, the PubMed database was searched. Using EHR data, we assessed and summarized deep learning performance in a number of clinical applications that focus on making specific predictions about clinical outcomes, and we compared the outcomes with those of conventional machine learning models. Results: For this study, a total of 57 papers were chosen. There have been five identified clinical outcome predictions: illness (n=33), intervention (n=6), mortality (n=5), Hospital readmission (n=7), and duration of stay (n=1). The majority of research (39 out of 57) used structured EHR data. RNNs were used as deep learning models the most frequently (LSTM: 17 studies, GRU: 6 research). The analysis shows that deep learning models have excelled when applied to a variety of clinical outcome predictions. While deep learning's application to EHR data has advanced rapidly, it's crucial that these models remain reliable, offering critical insights to assist clinicians in making informed decision. Conclusions: The findings demonstrate that deep learning can outperform classic machine learning techniques since it has the advantage of utilizing extensive and sophisticated datasets, such as longitudinal data seen in EHR. We think that deep learning will keep expanding because it has been quite successful in enhancing healthcare outcomes utilizing EHR data

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

Learning Better Clinical Risk Models.

Risk models are used to estimate a patient’s risk of suffering particular outcomes throughout clinical practice. These models are important for matching patients to the appropriate level of treatment, for effective allocation of resources, and for fairly evaluating the performance of healthcare providers. The application and development of methods from the field of machine learning has the potential to improve patient outcomes and reduce healthcare spending with more accurate estimates of patient risk. This dissertation addresses several limitations of currently used clinical risk models, through the identification of novel risk factors and through the training of more effective models. As wearable monitors become more effective and less costly, the previously untapped predictive information in a patient’s physiology over time has the potential to greatly improve clinical practice. However translating these technological advances into real-world clinical impacts will require computational methods to identify high-risk structure in the data. This dissertation presents several approaches to learning risk factors from physiological recordings, through the discovery of latent states using topic models, and through the identification of predictive features using convolutional neural networks. We evaluate these approaches on patients from a large clinical trial and find that these methods not only outperform prior approaches to leveraging heart rate for cardiac risk stratification, but that they improve overall prediction of cardiac death when considered alongside standard clinical risk factors. We also demonstrate the utility of this work for learning a richer description of sleep recordings. Additionally, we consider the development of risk models in the presence of missing data, which is ubiquitous in real-world medical settings. We present a novel method for jointly learning risk and imputation models in the presence of missing data, and find significant improvements relative to standard approaches when evaluated on a large national registry of trauma patients.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113326/1/alexve_1.pd

Abstracts - SA Heart Congress 2016

Index of abstracts: alphabetical listing of first authors

ICU prognostication: Time to re-evaluate? Register-based studies on improving prognostication for patients admitted to the intensive care unit (ICU)

Background: ICU prognostication is difficult because of patients’ prior comorbidities and their varied reasons for admission. The model used for ICU prognostication in Sweden is the Simplified Acute Physiology Score 3 (SAPS 3), which uses information gathered within one hour of ICU admission to predict 30-day mortality. Since the SAPS 3 model was introduced, no biomarkers have been added to it to improve its prognostic performance. For comatose patients admitted to the ICU after cardiac arrest, the prognostication performed after 72 h will either result in the continued observation of the patient or the withdrawal of life-sustaining treatment.Purpose: 1) To investigate whether adding the biomarker lactate (study I) or high-sensitivity troponin T (hsTnT) (study II) to SAPS 3 adds prognostic value. 2) To investigate whether using a supervised machine learning algorithm called artificial neural networks (ANNs) can improve the prognostic performance of SAPS 3 (study III). 3) To explore whether ANNs can create reliable predictions for comatose patients at the time of hospital admission (study IV) and during the first three days after ICU admission, with or without promising biomarkers (study V).Methods: 1) To investigate whether the laboratory values of lactate or hsTnT could improve the performance of SAPS 3, we combined patients’ laboratory values on ICU admission at Skåne University Hospital with their SAPS 3 score. 2) Based on all first-time ICU admissions in Sweden from 2009–2017 as retrieved from the Swedish Intensive Care Registry (SIR), we investigated whether ANNs could improve SAPS 3 using the same variables. 3) All out-of-hospital cardiac arrest (OHCA) patients from the Target Temperature Management trial were included for data analysis. Background and prehospital data, along with clinical variables at admission, were used in study IV. Clinical variables from the first three days were used in study V along with different levels of biomarkers defined as clinically accessible (e.g. neuron-specific enolase, or NSE) and research-grade biomarkers (e.g. neurofilament light, or NFL). Patient outcome was the dichotomised Cerebral Performance Category scale (CPC); a CPC of 1–2 was considered a good outcome, and a CPC of 3–5 was considered a poor outcome.Results: 1) Both lactate and hsTnT were independent SAPS 3 predictors for 30-day mortality in the logistic regression model. In a subgroup analysis, the use of lactate improved the area under the receiver operating characteristic curve (AUROC) for cardiac arrest and septic patients, and the use of hsTnT improved the AUROC for septic patients. 2) The overall performance of the SAPS 3 model in Sweden was improved by the use of ANNs. Both the discrimination (AUROC 0.89 vs 0.85, p < 0.001) and the calibration were improved when the two models were compared on a separate test set (n = 36,214). 3) An ANN model outperformed a logistic-regression-based model in predicting poor outcome on hospital admission for OHCA patients. Incorporating biomarkers such as NSE improved the AUROC over the course of the first three days of the ICU stay; when NFL was incorporated, the prognostic performance was excellent from day 1.Conclusion: Lactate and hsTnT probably add prognostic value to SAPS 3 for patients admitted to the ICU with sepsis or after cardiac arrest (lactate only). An ANN model was found to be superior to the SAPS 3 model (Swedish modification) and corrected better for age than SAPS 3. A simplified ANN model with eight variables showed performance similar to that of the SAPS 3 model. For comatose OHCA patients, an ANN model improved the accuracy of the prediction of the long-term neurological outcome at hospital admission. Furthermore, when it used cumulative information from the first three days after ICU admission, an ANN model showed promising prognostic performance on day 3 when it incorporated clinically accessible biomarkers such as NSE, and it showed promising performance on days 1–3 when it incorporated research-grade biomarkers such as NFL