Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

Early Prediction in Classification of Cardiovascular Diseases with Machine Learning, Neuro-Fuzzy and Statistical Methods

Timely and accurate detection of cardiovascular diseases (CVDs) is critically important to minimize the risk of a myocardial infarction. Relations between factors of CVDs are complex, ill-defined and nonlinear, justifying the use of artificial intelligence tools. These tools aid in predicting and classifying CVDs. In this article, we propose a methodology using machine learning (ML) approaches to predict, classify and improve the diagnostic accuracy of CVDs, including support vector regression (SVR), multivariate adaptive regression splines, the M5Tree model and neural networks for the training process. Moreover, adaptive neuro-fuzzy and statistical approaches, nearest neighbor/naive Bayes classifiers and adaptive neuro-fuzzy inference system (ANFIS) are used to predict seventeen CVD risk factors. Mixed-data transformation and classification methods are employed for categorical and continuous variables predicting CVD risk. We compare our hybrid models and existing ML techniques on a CVD real dataset collected from a hospital. A sensitivity analysis is performed to determine the influence and exhibit the essential variables with regard to CVDs, such as the patient's age, cholesterol level and glucose level. Our results report that the proposed methodology outperformed well known statistical and ML approaches, showing their versatility and utility in CVD classification. Our investigation indicates that the prediction accuracy of ANFIS for the training process is 96.56%, followed by SVR with 91.95% prediction accuracy. Our study includes a comprehensive comparison of results obtained for the mentioned methods.

Artificial neuronal network analysis in investigating the relationship between oxidative stress and endoplasmic reticulum stress to address blocked vessels in cardiovascular disease

BACKGROUND

Cardiovascular disease is the leading cause of death in the world and is associated with significant morbidity. Atherosclerosis is the main cause of cardiovascular disease (CVD), including myocardial infarction (MI), heart failure, and stroke. The mechanism of atherosclerosis has not been well investigated in different aspects, such as the relationship between oxidative stress and endothelial function. This project aims to investigate whether an oxidative enzyme vascular peroxidase 1 (VPO1) and activating transcription factor 4 (ATF4) can be used as biomarkers in highlighting the pathogenesis of the disease and in evaluating the prognosis of the relationship with endoplasmic reticulum and oxidative stress. This paper used artificial neural network analysis to predict cardiovascular disease risk based on new generation biochemical markers that combine vascular inflammation, oxidative and endoplasmic reticulum stress.

METHODS

For this purpose, 80 patients were evaluated according to the coronary angiography results. hs-CRP, lipid parameters and demographic characteristics, VPO1, ATF4 and Glutathione peroxidase 1(GPx1) levels were measured.

RESULTS

We found an increase in VPO1 and hs-CRP levels in single-vessel disease as compared to controls. On the contrary, ATF4 and GPx1 levels were decreased in the same group, which was not significant. Our results showed a significant positive correlation between ATF4 and lipid parameters. A statistically significant positive correlation was also observed for VPO1 and ATF4 (r=0.367, P<0.05), and a negative correlation was found for ATF4 and GPx1 (r=-0.467, P<0.01). A significant negative relationship was noted for GPx1 and hs-CRP in two/three-vessel disease (r=-0.366, P<0.05). Artificial neural network analysis stated that body mass index (BMI) and smoking history information give us an important clue as compared to age, gender and alcohol consumption parameters when predicting the number of blocked vessels.

CONCLUSIONS

VPO1 and ATF4 might be potential biomarkers associated with coronary artery disease, especially in the follow-up and monitoring of treatment protocols, in addition to traditional risk factors.

Effectiveness of Family-Centered Empowerment Model on Psychological Improvement of Patients With Myocardial Infarction: A Bayesian Multivariate Approach

Objective

There is a limited understanding of the impact of the family-centered empowerment model (FCEM) on the psychological symptoms in post-myocardial infarction (MI). This study aimed to evaluate the effectiveness of the FCEM on the psychological improvement of patients with MI.

Methods

The present study was a randomized controlled trial (RCT) where patients experienced a standard home cardiac rehabilitation (CR) or CR utilizing the FCEM approach. The empowerment of patients was estimated during nine assessments, such as pre- and post-intervention. Factors, such as quality of life (QoL), state and trait anxiety, and perceived stress, were evaluated. A Bayesian multivariate mixed-effects model was used to simultaneously investigate the effect of the intervention group on study outcomes across the time.

Results

Among all the participants in this study, 24 (34.3%) were women with a total mean ± standard deviation (SD) of 61.40 ± 12.83 and 24.87 ± 3.80 for age and body mass index (BMI). The participants who were in the FCEM group had a significantly higher mean level of perceived stress (β = 28.80), state anxiety (β = 16.20), trait anxiety (β = 3.65), physical (β = 38.54), and mental QoL (β = 42.14). Moreover, the individuals in the FCEM group had a significantly higher mean level of general health (β = 31.64) in the physical dimension of QoL, vitality (β = 15.04), mental role limitation (β = 21.84), and mental health (β = 18.16) in the mental dimension of QoL.

Conclusions

The FCEM can be a valuable treatment mechanism for patients with post-MI to improve their stress, anxiety, and QoL.

The Efficacy of Machine-Learning-Supported Smart System for Heart Disease Prediction

The disease may be an explicit status that negatively affects human health. Cardiopathy is one of the common deadly diseases that is attributed to unhealthy human habits compared to alternative diseases. With the help of machine learning (ML) algorithms, heart disease can be noticed in a short time as well as at a low cost. This study adopted four machine learning models, such as random forest (RF), decision tree (DT), AdaBoost (AB), and K-nearest neighbor (KNN), to detect heart disease. A generalized algorithm was constructed to analyze the strength of the relevant factors that contribute to heart disease prediction. The models were evaluated using the datasets Cleveland, Hungary, Switzerland, and Long Beach (CHSLB), and all were collected from Kaggle. Based on the CHSLB dataset, RF, DT, AB, and KNN models predicted an accuracy of 99.03%, 96.10%, 100%, and 100%, respectively. In the case of a single (Cleveland) dataset, only two models, namely RF and KNN, show good accuracy of 93.437% and 97.83%, respectively. Finally, the study used Streamlit, an internet-based cloud hosting platform, to develop a computer-aided smart system for disease prediction. It is expected that the proposed tool together with the ML algorithm will play a key role in diagnosing heart diseases in a very convenient manner. Above all, the study has made a substantial contribution to the computation of strength scores with significant predictors in the prognosis of heart disease.

Analysis and risk estimation system for heart attack using EDENN algorithm

Heart related diseases are very common in the present scenario. In the past two decades the number of heart patients have increased to a large extent. Due to this abrupt rise in the number of patients, the death count has also increased. Thus, an efficient and accurate system must be developed for the diagnosis of heart related diseases, as the present methods available are not accurate enough and are insufficient for the Heart Attack (HA) and its Risk Analysis (RA). This paper propounds a system for HA risk estimation by the use of an Enhanced Deep Elman Neural Network (EDENN). In this system a Photoplethysmography (PPG) signal is inputted and pre-processed for noise removal. Further, Signal Decomposition (SD) is done, and the vital signs are estimated like Blood Pressure (BP), Respiratory Rate (RR) and Cardiac Autonomic Nervous System (CANS). For the BP estimation, Modified Maximum Amplitude Algorithm (MMAA) method is used and for the decomposed signal processing the Improved Incremental Merge Segmentation (IIMS) is used. As for features, Variation of amplitude, frequency and intensity are calculated and merged.

Significance of Visible Non-Invasive Risk Attributes for the Initial Prediction of Heart Disease Using Different Machine Learning Techniques

Introduction

Heart disease is emerging as the single most critical cause of death worldwide and is one of the costliest chronic conditions.

Purpose

Stimulated by the increasing heart disease mortality rate incidents, an effective, low-cost, and reliable heart disease risk evaluation model is developed using significant non-invasive risk attributes. The significant non-invasive risk attributes like (age, systolic BP, diastolic BP, BMI, hereditary factor, smoking, alcohol, and physical inactivity) are identified by the help of medical domain experts, and their reliability in heart disease prediction is investigated through different feature selection techniques. Methodology. The enhancements of applying specific investigated techniques like random forest, Naïve Bayes, decision tree, support vector machine, and K nearest neighbor to the risk factors are tested. The heart disease risk assessment model is developed using the Jupyter Notebook web application, and its performance is tested not only through medical domain measures but also through the model performance measures. Findings. To evaluate heart disease risk evaluation model, we calculated measures of discrimination like error rate, AUROC, sensitivity, specificity, accuracy, precision, and so on. Experimental results show that the random forest heart disease risk evaluation model outperforms other existing risk models with admirable predictive accuracy and minimum misclassification rate. Originality. The heart disease risk evaluation model is developed based on novel non-invasive heart disease dataset, which consists of 5776 records. This dataset is collected from different heterogeneous data sources of Kashmir (India) through quantitative data collection methods. Research Implications. The risk model is applicable where people lack the facilities of integrated primary medical care technologies for untimely heart disease risk prediction. Future Work. To investigate deep learning and study the significance of other controlled attributes on different age and sex groups in the risk estimation of heart disease.

A Systematic Review on Prediction Techniques for Cardiac Disease

Mortality rate can be lowered with early prediction of cardiac diseases, which is one of the major issue in healthcare industry. In comparison of traditional methods, intelligent systems have potential to predict these diseases accurately at early stage even with complex data. Various intelligent DSS are presented by researchers for predicting this disease. To study the trends of these intelligent systems, to find the effective techniques for predicting cardiac disease and to find the future directions are the objective of this study. Therefore this paper presents a systematic review on state-of-art techniques based on ML, NN and FL. For analysis, we follow PRISMA statement and considered the studies presented from 2010 to 2020 from different databases. Analysis concluded that ML based techniques are broadly used for feature selection and classification and have the potential for the prediction of cardiac diseases. The future directions are to evaluate the rarely used prediction techniques and finding the way of improving them for model generalization with better prediction accuracy.

Computer-Aided Diagnostics of Heart Disease Risk Prediction Using Boosting Support Vector Machine

Heart diseases are a leading cause of death worldwide, and they have sparked a lot of interest in the scientific community. Because of the high number of impulsive deaths associated with it, early detection is critical. This study proposes a boosting Support Vector Machine (SVM) technique as the backbone of computer-aided diagnostic tools for more accurately forecasting heart disease risk levels. The datasets which contain 13 attributes such as gender, age, blood pressure, and chest pain are taken from the Cleveland clinic. In total, there were 303 records with 6 tuples having missing values. To clean the data, we deleted the 6 missing records through the listwise technique. The size of data, and the fact that it is a purely random subset, made this approach have no significant effect for the experiment because there were no biases. Salient features are selected using the boosting technique to speed up and improve accuracies. Using the train/test split approach, the data is then partitioned into training and testing. SVM is then used to train and test the data. The C parameter is set at 0.05 and the linear kernel function is used. Logistic regression, Nave Bayes, decision trees, Multilayer Perceptron, and random forest were used to compare the results. The proposed boosting SVM performed exceptionally well, making it a better tool than the existing techniques.

Prediction of coronary heart disease in rural Chinese adults: a cross sectional study

Background

Coronary heart disease (CHD) is a common cardiovascular disease with high morbidity and mortality in China. The CHD risk prediction model has a great value in early prevention and diagnosis.

Methods

In this study, CHD risk prediction models among rural residents in Xinxiang County were constructed using Random Forest (RF), Support Vector Machine (SVM), and the least absolute shrinkage and selection operator (LASSO) regression algorithms with identified 16 influencing factors.

Results

Results demonstrated that the CHD model using the RF classifier performed best both on the training set and test set, with the highest area under the curve (AUC = 1 and 0.9711), accuracy (one and 0.9389), sensitivity (one and 0.8725), specificity (one and 0.9771), precision (one and 0.9563), F1-score (one and 0.9125), and Matthews correlation coefficient (MCC = one and 0.8678), followed by the SVM (AUC = 0.9860 and 0.9589) and the LASSO classifier (AUC = 0.9733 and 0.9587). Besides, the RF model also had an increase in the net reclassification index (NRI) and integrated discrimination improvement (IDI) values, and achieved a greater net benefit in the decision curve analysis (DCA) compared with the SVM and LASSO models.

Conclusion

The CHD risk prediction model constructed by the RF algorithm in this study is conducive to the early diagnosis of CHD in rural residents of Xinxiang County, Henan Province.

A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome

OBJECTIVE

Some researchers have studied about early prediction and diagnosis of major adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this paper proposes a soft voting ensemble classifier (SVE) using machine learning (ML) algorithms.

METHODS

We used the Korea Acute Myocardial Infarction Registry dataset and selected 11,189 subjects among 13,104 with the 2-year follow-up. It was subdivided into two groups (ST-segment elevation myocardial infarction (STEMI), non ST-segment elevation myocardial infarction NSTEMI), and then subdivided into training (70%) and test dataset (30%). Third, we selected the ranges of hyper-parameters to find the best prediction model from random forest (RF), extra tree (ET), gradient boosting machine (GBM), and SVE. We generated each ML-based model with the best hyper-parameters, evaluated by 5-fold stratified cross-validation, and then verified by test dataset. Lastly, we compared the performance in the area under the ROC curve (AUC), accuracy, precision, recall, and F-score.

RESULTS

The accuracies for RF, ET, GBM, and SVE were (88.85%, 88.94%, 87.84%, 90.93%) for complete dataset, (84.81%, 85.00%, 83.70%, 89.07%) STEMI, (88.81%, 88.05%, 91.23%, 91.38%) NSTEMI. The AUC values in RF were (98.96%, 98.15%, 98.81%), ET (99.54%, 99.02%, 99.00%), GBM (98.92%, 99.33%, 99.41%), and SVE (99.61%, 99.49%, 99.42%) for complete dataset, STEMI, and NSTEMI, respectively. Consequently, the accuracy and AUC in SVE outperformed other ML models.

CONCLUSIONS

The performance of our SVE was significantly higher than other machine learning models (RF, ET, GBM) and its major prognostic factors were different. This paper will lead to the development of early risk prediction and diagnosis tool of MACE in ACS patients.

Cardiac well-being indexes: a decision support tool to monitor cardiovascular health

Despite the increasing awareness about its severity and the importance of adopting preventive habits, cardiovascular disease remains the leading cause of death worldwide. Most people already recognize that a healthy lifestyle, which includes a balanced diet and the practice of physical activity, is essential to prevent this disease. However, since few simple mechanisms allow a self-assessment and a continuous monitoring of the level of cardiac well-being, people are not conscious enough about their own cardiovascular health status. In this context, this paper presents and describes a tool related to the creation of cardiac well-being indexes that allow a quick and intuitive monitoring and visualization of the users’ cardiovascular health level over time. For its implementation, data mining techniques were used to calculate the indexes, and a data warehouse was built to archive the data and to support the construction of dashboards for presenting the results.

Reviewing the use and quality of machine learning in developing clinical prediction models for cardiovascular disease

Cardiovascular disease (CVD) is one of the leading causes of death across the world. CVD can lead to angina, heart attacks, heart failure, strokes, and eventually, death; among many other serious conditions. The early intervention with those at a higher risk of developing CVD, typically with statin treatment, leads to better health outcomes. For this reason, clinical prediction models (CPMs) have been developed to identify those at a high risk of developing CVD so that treatment can begin at an earlier stage. Currently, CPMs are built around statistical analysis of factors linked to developing CVD, such as body mass index and family history. The emerging field of machine learning (ML) in healthcare, using computer algorithms that learn from a dataset without explicit programming, has the potential to outperform the CPMs available today. ML has already shown exciting progress in the detection of skin malignancies, bone fractures and many other medical conditions. In this review, we will analyse and explain the CPMs currently in use with comparisons to their developing ML counterparts. We have found that although the newest non-ML CPMs are effective, ML-based approaches consistently outperform them. However, improvements to the literature need to be made before ML should be implemented over current CPMs.

Coronary heart disease diagnosis by artificial neural networks including aortic pulse wave velocity index and clinical parameters

BACKGROUND

Cardiovascular disease, such as coronary heart disease (CHD), are the main cause of mortality and morbidity worldwide. CHD is not entirely predicted by classic risk factors; however, they are preventable. Facing this major problem, the development of novel methods for CHD risk prediction is of practical interest. The purpose of our study was to construct an artificial neural networks (ANNs)-based diagnostic model for CHD risk using a complex of clinical and haemodynamics factors of this disease and aortic pulse wave velocity (PWV) index.

METHODS

A total of 437 patients were included from 2012 to 2017: 99 CHD and 338 non-CHD patients. Theoretical PWV was calculated, on 93 patients free of hypertension, diabetes and CHD, according to age, blood pressure, sex and heart rate. The results were expressed as an index [(measured PWV - theoretical PWV)/theoretical PWV] for each patient. The original database for ANNs included clinical, haemodynamic and laboratory characteristics. Multilayered perceptron ANNs architecture were applied. The performance of prediction was evaluated by accuracy values based on standard definitions.

RESULTS

By changing the types of ANNs and the number of input factors applied, we created models that demonstrated 0.63-0.93 accuracy. The best accuracy was obtained with ANNs topology of multilayer perceptron with three hidden layers for models, parameters included by both biological factors, carotid plaque and PWV index.

CONCLUSION

ANNs models including a PWV index could be used as promising approaches for predicting CHD risk without the need for invasive diagnostic methods and may help in the clinical decision.

Deep learning approach for diabetes prediction using PIMA Indian dataset

PURPOSE

International Diabetes Federation (IDF) stated that 382 million people are living with diabetes worldwide. Over the last few years, the impact of diabetes has been increased drastically, which makes it a global threat. At present, Diabetes has steadily been listed in the top position as a major cause of death. The number of affected people will reach up to 629 million i.e. 48% increase by 2045. However, diabetes is largely preventable and can be avoided by making lifestyle changes. These changes can also lower the chances of developing heart disease and cancer. So, there is a dire need for a prognosis tool that can help the doctors with early detection of the disease and hence can recommend the lifestyle changes required to stop the progression of the deadly disease.

METHOD

Diabetes if untreated may turn into fatal and directly or indirectly invites lot of other diseases such as heart attack, heart failure, brain stroke and many more. Therefore, early detection of diabetes is very significant so that timely action can be taken and the progression of the disease may be prevented to avoid further complications. Healthcare organizations accumulate huge amount of data including Electronic health records, images, omics data, and text but gaining knowledge and insight into the data remains a key challenge. The latest advances in Machine learning technologies can be applied for obtaining hidden patterns, which may diagnose diabetes at an early phase. This research paper presents a methodology for diabetes prediction using a diverse machine learning algorithm using the PIMA dataset.

RESULTS

The accuracy achieved by functional classifiers Artificial Neural Network (ANN), Naive Bayes (NB), Decision Tree (DT) and Deep Learning (DL) lies within the range of 90-98%. Among the four of them, DL provides the best results for diabetes onset with an accuracy rate of 98.07% on the PIMA dataset. Hence, this proposed system provides an effective prognostic tool for healthcare officials. The results obtained can be used to develop a novel automatic prognosis tool that can be helpful in early detection of the disease.

CONCLUSION

The outcome of the study confirms that DL provides the best results with the most promising extracted features. DL achieves the accuracy of 98.07% which can be used for further development of the automatic prognosis tool. The accuracy of the DL approach can further be enhanced by including the omics data for prediction of the onset of the disease.

Nationwide prediction of type 2 diabetes comorbidities

Identification of individuals at risk of developing disease comorbidities represents an important task in tackling the growing personal and societal burdens associated with chronic diseases. We employed machine learning techniques to investigate to what extent data from longitudinal, nationwide Danish health registers can be used to predict individuals at high risk of developing type 2 diabetes (T2D) comorbidities. Leveraging logistic regression-, random forest- and gradient boosting models and register data spanning hospitalizations, drug prescriptions and contacts with primary care contractors from >200,000 individuals newly diagnosed with T2D, we predicted five-year risk of heart failure (HF), myocardial infarction (MI), stroke (ST), cardiovascular disease (CVD) and chronic kidney disease (CKD). For HF, MI, CVD, and CKD, register-based models outperformed a reference model leveraging canonical individual characteristics by achieving area under the receiver operating characteristic curve improvements of 0.06, 0.03, 0.04, and 0.07, respectively. The top 1,000 patients predicted to be at highest risk exhibited observed incidence ratios exceeding 4.99, 3.52, 1.97 and 4.71 respectively. In summary, prediction of T2D comorbidities utilizing Danish registers led to consistent albeit modest performance improvements over reference models, suggesting that register data could be leveraged to systematically identify individuals at risk of developing disease comorbidities.

Reconstruction error based deep neural networks for coronary heart disease risk prediction

Coronary heart disease (CHD) is one of the leading causes of death worldwide; if suffering from CHD and being in its end-stage, the most advanced treatments are required, such as heart surgery and heart transplant. Moreover, it is not easy to diagnose CHD at the earlier stage; hospitals diagnose it based on various types of medical tests. Thus, by predicting high-risk people who are to suffer from CHD, it is significant to reduce the risks of developing CHD. In recent years, some research works have been done using data mining to predict the risk of developing diseases based on medical tests. In this study, we have proposed a reconstruction error (RE) based deep neural networks (DNNs); this approach uses a deep autoencoder (AE) model for estimating RE. Initially, a training dataset is divided into two groups by their RE divergence on the deep AE model that learned from the whole training dataset. Next, two DNN classifiers are trained on each group of datasets separately by combining a RE based new feature with other risk factors to predict the risk of developing CHD. For creating the new feature, we use deep AE model that trained on the only high-risk dataset. We have performed an experiment to prove how the components of our proposed method work together more efficiently. As a result of our experiment, the performance measurements include accuracy, precision, recall, F-measure, and AUC score reached 86.3371%, 91.3716%, 82.9024%, 86.9148%, and 86.6568%, respectively. These results show that the proposed AE-DNNs outperformed regular machine learning-based classifiers for CHD risk prediction.

Automated Diagnosis of Coronary Artery Disease: A Review and Workflow

Coronary artery disease (CAD) is the most dangerous heart disease which may lead to sudden cardiac death. However, CAD diagnoses are quite expensive and time-consuming procedures which a patient need to go through. The aim of our paper is to present a unique review of state-of-the-art methods up to 2017 for automatic CAD classification. The protocol of review methods is identifying best methods and classifier for CAD identification. The study proposes two workflows based on two parameter sets for instances A and B. It is necessary to follow the proper procedure, for future evaluation process of automatic diagnosis of CAD. The initial two stages of the parameter set A workflow are preprocessing and feature extraction. Subsequently, stages (feature selection and classification) are same for both workflows. In literature, the SVM classifier represents a promising approach for CAD classification. Moreover, the limitation leads to extract proper features from noninvasive signals.