Heart Disease Risk Prediction Research Articles

Prediction of Heart Disease Risk among Patients in Federal Medical Centre, Abeokuta Using Naïve Bayes

Heart disease is a silent killer, which can cause sudden death of individuals without obvious symptoms. The risk of heart disease is a sign in human beings that must not be neglected. Therefore, this study aimed to predict heart disease among patients in the Federal Medical Hospital Centre (FMC), Abeokuta. Descriptive statistics and data visualization techniques were used to gain insights into the distribution and relationships among the variables. Subsequently, a Naive Bayes classifier model was built using 80% of the data for training and 20% for testing. In addition, a Decision Tree Algorithm (DT) model was used to compare the performance of the Naive Bayes model. The performances of the two models were evaluated using accuracy, sensitivity, ROC-AUC, specificity, precision, and the F1-score. The Naive Bayes model achieved an overall accuracy of 83.61%, precision of 89.29%, recall of 78.12%, F1-Score of 83.33%, ROC-AUC of 90%, sensitivity of 78.12%, and specificity of 89.66. On the other hand, they were compared with the Decision Tree (DT) model which achieved an overall accuracy of 75.41%, precision of 77.42%, recall of 75%, F1-Score of 76.19%, ROC-AUC of 84.54%, sensitivity of 75%, and specificity of 75.86%. Similarly, the confusion matrix for both analyses gave the correct classification of 25 and 22 cases of patients who have heart disease while their wrong classification was 7 and 10 cases of patients who have no heart disease respectively. Furthermore, the importance of features carried out on both showed that the most significant features are the maximum heart rate achieved, fasting blood sugar, resting blood pressure, and chest pain respectively for Naive Bayes and Decision Tree. The findings of the analysis showed that the Naive Bayes model outperformed the Decision Tree in every aspect of the analyses in predicting the risk of heart disease based on the data used and, it suggested that medical health insurance should consider incorporating predictive modelling techniques like Naive Bayes into their risk assessment algorithms, which can be of great use in the medical line.

Optimization of multidimensional feature engineering and data partitioning strategies in heart disease prediction models

The relentless rise in heart disease incidence, a leading global cause of death, presents a significant public health challenge. Precise prediction of heart disease risk and early interventions are crucial. This study investigates the performance improvement of heart disease prediction models using machine learning and deep learning algorithms. Initially, we utilized the Heart Failure Prediction Dataset from Kaggle. After preprocessing to ensure data quality, three distinct feature engineering techniques were applied: PCA for dimensionality reduction, ET for feature selection, and Pearson's correlation coefficient for feature selection. We assessed their impact on model performance. The dataset was then partitioned into three different data split ratios—1:9, 2:8, and 3:7—to determine their specific effects on model performance. Twelve machine learning classifiers—LGBM, Adaboost, XGB, RF, DT, KNN, LR, GNB, ET, SVC, GB, and Bagging—were trained and evaluated based on five key metrics: accuracy, recall, precision, F1 score, and training time. The influence of different feature engineering methods and data partitioning ratios on model performance were systematically analyzed using paired-sample t-tests. Among the feature engineering methods compared, the Bagging classifier, when combined with feature selection via ET, exhibited superior performance. It achieved an accuracy of 97.48 % and an F1-Score of 97.48 % with a data split ratio of 1:9 between the test and training sets. With a 2:8 split, the accuracy was 94.96 % and the F1-Score was 94.95 %. For a 3:7 split, the accuracy was 94.12 % and the F1-Score was 94.11 %. Paired sample T-test results indicate that feature selection using Pearson correlation coefficient can shorten training duration, but this also leads to a decline in classifier performance. After applying PCA dimensionality reduction, compared with the control group, there was no significant difference in the training efficiency and efficacy of the classifier. However, feature selection through ET significantly reduced the training time for various classifiers while ensuring their performance.

Heart Disease Prediction Using Machine Learning

Heart disease continues to be a major global cause of death, highlighting the urgent need for more effective methods of early detection and risk evaluation. This paper explores the use of machine learning techniques for predicting heart disease, focusing on five key algorithms: Naïve Bayes, k-Nearest Neighbor (KNN), Decision Tree, Artificial Neural Network (ANN), and Random Forest. Through a comprehensive review of existing research and data, we assess the performance of these algorithms in heart disease risk prediction. The analysis reveals that machine learning approaches offer substantial improvements in both accuracy and efficiency over traditional diagnostic techniques. Among the algorithms studied, Random Forest showed the best overall results, with some studies indicating accuracy rates as high as 95% in detecting potential heart disease cases. This review underscores the transformative potential of machine learning in reshaping cardiovascular healthcare through more individualized risk assessments and the promotion of early intervention strategies. Incorporating these advanced predictive models into routine clinical workflows could lead to significantly better patient outcomes and help alleviate the worldwide impact of heart disease. Keywords: Cardiovascular Risk Assessment, Machine Learning Models, Health Data Analytics, Random Forest, Neural Networks, Feature Significance, Decision Support Systems, Precision Medicine, Predictive Healthcare Analytics, Early Diagnosis.

Addition of Social Determinants of Health to Coronary Heart Disease Risk Prediction: The Multi-Ethnic Study of Atherosclerosis.

Social determinants of health (SDoH) are associated with cardiovascular risk factors and outcomes; however, they are absent from risk prediction models. We aimed to assess if the addition of SDoH improves the predictive ability of the MESA (Multi-Ethnic Study of Atherosclerosis) Risk Score. This was a community-based prospective population cohort study that enrolled 6286 men and women, ages 45-84 years, who were free of clinical coronary heart disease (CHD) at baseline. Data from 10-year follow-up were examined for CHD events, defined as myocardial infarction, fatal CHD, resuscitated cardiac arrest, and revascularization in cases of anginal symptoms. Participants included 53% women with average age of 62 years. When adjusting for traditional cardiovascular risk factors, SDoH, and coronary artery calcium, economic strain, specifically low family income, was associated with a greater risk of CHD events (hazard ratio [HR], 1.42 [95% CI, 1.17-1.71], P value<0.001). Area under the curve of risk prediction with SDoH was 0.822, compared with 0.816 without SDoH. The calibration slope was 0.860 with SDoH and 0.878 in the original model. Significant associations were found between economic/financial SDoH and CHD risk factors and outcomes. Incorporation of SDoH into the MESA Risk Score did not improve predictive ability of the model. Our findings do not support the incorporation of SDoH into current risk prediction algorithms.

Enhancing Coronary Heart Disease Risk Prediction with Machine Learning and Deep Learning

Enhancing Coronary Heart Disease Risk Prediction with Machine Learning and Deep Learning

Adapting transformer-based language models for heart disease detection and risk factors extraction

Efficiently treating cardiac patients before the onset of a heart attack relies on the precise prediction of heart disease. Identifying and detecting the risk factors for heart disease such as diabetes mellitus, Coronary Artery Disease (CAD), hyperlipidemia, hypertension, smoking, familial CAD history, obesity, and medications is critical for developing effective preventative and management measures. Although Electronic Health Records (EHRs) have emerged as valuable resources for identifying these risk factors, their unstructured format poses challenges for cardiologists in retrieving relevant information. This research proposed employing transfer learning techniques to automatically extract heart disease risk factors from EHRs. Leveraging transfer learning, a deep learning technique has demonstrated a significant performance in various clinical natural language processing (NLP) applications, particularly in heart disease risk prediction. This study explored the application of transformer-based language models, specifically utilizing pre-trained architectures like BERT (Bidirectional Encoder Representations from Transformers), RoBERTa, BioClinicalBERT, XLNet, and BioBERT for heart disease detection and extraction of related risk factors from clinical notes, using the i2b2 dataset. These transformer models are pre-trained on an extensive corpus of medical literature and clinical records to gain a deep understanding of contextualized language representations. Adapted models are then fine-tuned using annotated datasets specific to heart disease, such as the i2b2 dataset, enabling them to learn patterns and relationships within the domain. These models have demonstrated superior performance in extracting semantic information from EHRs, automating high-performance heart disease risk factor identification, and performing downstream NLP tasks within the clinical domain. This study proposed fine-tuned five widely used transformer-based models, namely BERT, RoBERTa, BioClinicalBERT, XLNet, and BioBERT, using the 2014 i2b2 clinical NLP challenge dataset. The fine-tuned models surpass conventional approaches in predicting the presence of heart disease risk factors with impressive accuracy. The RoBERTa model has achieved the highest performance, with micro F1-scores of 94.27%, while the BERT, BioClinicalBERT, XLNet, and BioBERT models have provided competitive performances with micro F1-scores of 93.73%, 94.03%, 93.97%, and 93.99%, respectively. Finally, a simple ensemble of the five transformer-based models has been proposed, which outperformed the most existing methods in heart disease risk fan, achieving a micro F1-Score of 94.26%. This study demonstrated the efficacy of transfer learning using transformer-based models in enhancing risk prediction and facilitating early intervention for heart disease prevention.

Empirical exploration of whale optimisation algorithm for heart disease prediction

Heart Diseases have the highest mortality worldwide, necessitating precise predictive models for early risk assessment. Much existing research has focused on improving model accuracy with single datasets, often neglecting the need for comprehensive evaluation metrics and utilization of different datasets in the same domain (heart disease). This research introduces a heart disease risk prediction approach by harnessing the whale optimization algorithm (WOA) for feature selection and implementing a comprehensive evaluation framework. The study leverages five distinct datasets, including the combined dataset comprising the Cleveland, Long Beach VA, Switzerland, and Hungarian heart disease datasets. The others are the Z-AlizadehSani, Framingham, South African, and Cleveland heart datasets. The WOA-guided feature selection identifies optimal features, subsequently integrated into ten classification models. Comprehensive model evaluation reveals significant improvements across critical performance metrics, including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic curve. These enhancements consistently outperform state-of-the-art methods using the same dataset, validating the effectiveness of our methodology. The comprehensive evaluation framework provides a robust assessment of the model’s adaptability, underscoring the WOA’s effectiveness in identifying optimal features in multiple datasets in the same domain.

Comparison of seven surrogate insulin resistance indexes for prediction of incident coronary heart disease risk: a 10-year prospective cohort study.

There were seven novel and easily accessed insulin resistance (IR) surrogates established, including the Chinese visceral adiposity index (CVAI), the visceral adiposity index (VAI), lipid accumulation product (LAP), triglyceride glucose (TyG) index, TyG-body mass index (TyG-BMI), TyG-waist circumference (TyG-WC) and TyG-waist to height ratio (TyG-WHtR). We aimed to explore the association between the seven IR surrogates and incident coronary heart disease (CHD), and to compare their predictive powers among Chinese population. This is a 10-year prospective cohort study conducted in China including 6393 participants without cardiovascular disease (CVD) at baseline. We developed Cox regression analyses to examine the association of IR surrogates with CHD (hazard ratio [HR], 95% confidence intervals [CI]). Moreover, the receiver operating characteristic (ROC) curve was performed to compare the predictive values of these indexes for incident CHD by the areas under the ROC curve (AUC). During a median follow-up period of 10.25 years, 246 individuals newly developed CHD. Significant associations of the IR surrogates (excepted for VAI) with incident CHD were found in our study after fully adjustment, and the fifth quintile HRs (95% CIs) for incident CHD were respectively 2.055(1.216-3.473), 1.446(0.948-2.205), 1.753(1.099-2.795), 2.013(1.214-3.339), 3.169(1.926-5.214), 2.275(1.391-3.719) and 2.309(1.419-3.759) for CVAI, VAI, LAP, TyG, TyG-BMI, TyG-WC and TyG-WHtR, compared with quintile 1. Furthermore, CVAI showed maximum predictive capacity for CHD among these seven IR surrogates with the largest AUC: 0.632(0.597,0.667). The seven IR surrogates (excepted for VAI) were independently associated with higher prevalence of CHD, among which CVAI is the most powerful predictor for CHD incidence in Chinese populations.

From ejection fraction, to myocardial strain, and myocardial work in echocardiography: Clinical impact and controversies.

Enhancing an echocardiographic tool, aimed to detect even subtle left ventricular (LV) systolic function abnormalities, capable of obtaining both early diagnosis and risk prediction of heart disease, represents an ambitious, attractive, and arduous purpose in the modern era of cardiovascular imaging. Ideally, that tool should be simple, reliable, and reproducible, in order to be concretely applied in routine clinical practice. Importantly, that technique should be physiologically plausible and useful both at the population-level, as well as in the individual subject. For a long time, LV ejection fraction (EF) has been considered the first-line parameter for assessing LV global systolic function, strictly related to the prognosis, at least in some settings. However, LV EF limitations are well-known, even though frequently overemphasized, including its load-dependency. Therefore, myocardial strain techniques have been proposed, deemed able to disclose even subtle early LV function anomalies. Nevertheless, many disadvantages of myocardial strain have been reported as well. More recently, myocardial work (MW) analysis has been introduced as a new echocardiographic tool for the evaluation of LV global systolic function, attempting to overcome EF and strain disadvantages. However, MW has shown many limits as well. Notwithstanding, LV EF still remains a landmark functional classification marker for heart failure and cardiac oncology, allowing reliable fast reassessment of LV function changes during patient management, in order to guide treatment in individual cases as well. Notably, global longitudinal strain and MW parameters seem to show better meaningful results at the population-level, but controversial clinical impact, major limitations, wide cut-offs spread and overlap, when the single value needs to be applied to the single case. Taking into account the recent literature-based evidence, the scope of the present narrative critical review is trying to delineate the different types of information given by the described LV global systolic function parameters, both at the population-level and in the individual case, in order to trace a comparative analysis of advantages and limitations in clinical practice.

The predictive accuracy of coronary heart disease risk prediction models in rural Northwestern China

Cardiovascular risk models developed may have limitations when applied to rural Chinese. This study validated and compared the Framingham Risk Score (FRS) and Prediction for Atherosclerotic Cardiovascular Disease Risk in China (PAR) models in predicting 10-year risk of coronary heart disease (CHD) in a rural cohort in Ningxia, China from 2008 to 2019. The FRS and PAR models were validated by estimating predicted events, C index, calibration χ2 and plots. 1381 adults without CHD at baseline were followed up for 9.75 years on average. 168 CHD cases were observed. The FRS and PAR underestimated CHD events by 22 % and 46 % for the total population, while overestimated for males by 152 % and 78 %, respectively. The C index was slightly higher for PAR than FRS. Both models showed weak calibration with chi-square values above 20 (p < 0.001). Bland-Altman plots indicated FRS predicted higher CHD risk than PAR, lacking consistency. Overall, FRS and PAR demonstrated limited performance in predicting 10-year CHD risk in this rural population. PAR had slightly better discrimination than FRS, but require further improvement in calibration and individual risk estimation to suit the rural population in Northwest China.

Abstract 11652: The Addition of Social Determinants of Health to Coronary Heart Disease Risk Prediction

Introduction: Coronary heart disease (CHD) remains a leading cause of death worldwide, with an unequal distribution of this burden placed on individuals of lower socioeconomic status. Individual social determinants of health (SDoH) have been shown to be associated with CHD; however, whether the addition of SDoH can improve current CHD risk prediction algorithms has not been delineated. Hypothesis: We hypothesize that the addition of SDoH to the Multi-Ethnic Study of Atherosclerosis (MESA) risk score improves the prediction of 10-year CHD events. Methods: In 6,286 individuals from the MESA cohort, we evaluated the additional predictive value of SDoH beyond the traditional factors included in the existing MESA Risk Score. SDoH considered were employment, family income, house tenure, financial strain, education, marital status, social support, chronic stress, discrimination, neighborhood characteristics, healthcare access, and health insurance, which were organized into five composite domains. A penalized Cox proportional hazards model was used to select SDoH variables with the potential to improve prediction, then an unpenalized Cox regression model was fitted with traditional risk factors plus the selected SDoH variables. Results: Statistically significant differences in the distribution of SDoH were observed between racial and ethnic groups. Notably, unfavorable SDoH were concentrated in Black and Hispanic/Latino persons. Economic stability, education, and community and social context were associated with CHD events. The area under the curve (AUC) for prediction of a 10-year CHD event was 0.816 without SDoH factors and 0.818 with the addition of SDoH. Conclusions: Racial and ethnic differences in social determinants of health were obsserved. Economic stability, education, and community and social context were significantly associated with cardiovascular events, however the addition of SDoH did not significantly improve CHD risk prediction.

Visualization analysis and logistic regression based heart disease risk prediction

Heart-related illness is the major cause of global death. The optimal solution to tackle this problem and to improve public health is early detection and prevention. Manually diagnosis is tedious and time consuming, which is difficult to be applied for large scale medical inspections, and hence machine learning, computer-based automatic algorithms could be adopted. Logistic regression is a commonly used statistical method for predicting the risk of binary outcomes, such as the presence or absence of heart problems. In this study, logistic regression is leveraged to a dataset of medical records. It is not only developed as an effective model for the early detection of heart disease, but also leveraged for identifying the crucial risk factors of the disease. The results showed that the logistic regression model achieved a high level of accuracy for heart risk prediction, which overall accuracy is 85%. Factors including sex, cholesterol level, age, and blood pressure are observed possessing highest correlations with heart disease.

Enhanced Recurrent Neural Network (RNN) For Heart Disease Risk Prediction Using Framingham Datasets

Heart disease is one of the leading causes of death globally, which takes 17.9 million lives each year. The existing heart disease prediction techniques have a gap that does not consider the smoking attributes from the heart disease data. So, the accuracy is based on the limited number of medical data and the deep learning model. The existing deep learning models which use the Recurrent Neural Network (RNN) for heart disease prediction consume more processing and analysing time, mainly due to the delay of data retrieval. This delay causes the prediction process to become slower and leads to a moderate prediction only. The backpropagation of the RNN with an update gate and internal memory to carry the updated data cause a minor data glitch that leads to lower accuracy. Therefore, an efficient heart disease prediction model is very crucial to provide early detection among patients. This research proposes a Heart Disease Risk Prediction Model (HDRPM) with an enhanced RNN to improve the prediction accuracy using Framingham heart disease datasets. The specificity and sensitivity are imposed to improve the quality of the predictions. Sensitivity measure is used for detecting patients with heart disease perfectly and specificity measure is used for detecting patients without the disease perfectly. Besides the accuracy and quality of the prediction problem, the imbalance of minority classes in the dataset occurred in most deep learning prediction fields. This research aims to improve the quality of imbalanced Framingham datasets using Synthetic Minority Over-sampling Technique (SMOTe), which will synthetic instances in a small class to be equalized. The existing RNN model faces vanishing gradients that impede the learning of long data sequences. These gradients that carry information in the RNN cells will become smaller gradually till it minimises the parameter updates and leads to poor learning. For this purpose, the presence of multiple Gated Recurrent Unit (GRU) is used to overcome the vanishing gradients and ensure the hidden layers. The neurons of RNN rapidly cater for the essential information during the training and validation phase of the HDRPM. The integration of multiple GRU with the RNN, operating on the Tensorflow as back-end and Keras as the core for the neural network library has increased the performance of the proposed model. The proposed model provides up to 98.78%, the highest accuracy achieved compared to related previous work, which is a quantum neural network model with 98.57. This HDRPM is expected to significantly contribute to early detection of heart disease patients.

Real Time Risk Prediction of heart patients using HRV and IoT: A Survey

Abstract: In today's world, the leading cause of death is heart disease. The term heart disease is described as several different conditions that affect the heart. There are many types of heart disease, but the most common are coronary heart disease, congestive heart failure, and arrhythmia. Currently, the healthcare system is hospital essential which is inefficient to treatthese conditions, which require immediate assistance. This is a roundabout way to point to a rise in the death rates. Heart rate variability (HRV) is a useful tool for identifying and monitoring potential risk factors for heart disease. HRV measures the time between each heartbeat and can be used to identify subtle changes in heart rate that may be indicative of an underlying health condition. With the help of Machine learning and (Internet of Things) IoT-based heart patients real-time monitoring and predicting risk analysis will enable doctors to view patient's health status online. Internet of Things (IoT) technology to continuously track and monitor vital signs,including heart rate, pulse rate, and body temperature. In addition to these traditional vital signs, the system also includes afeature for monitoring heart rate variability (HRV), which has been shown to be a useful indicator of heart disease risk prediction. Using machine learning and HRV capabilities, the system will be able to assess health status and anticipate patient'sheart health (low-risk or high-risk) based on the ECG waveform and HRV characteristics such as time domain and frequency domain values

Relationship between serum high-sensitivity C-reactive protein (hs-CRP) and glycated hemoglobin (HbA1c) in South Indian population

Introduction and Aim: Cardiovascular disease are a leading cause of death in the world. Atherosclerosis plays the most important role in its pathophysiology. It is a slowly progressing chronic inflammatory process resulting in increased levels of inflammatory markers in serum. hs-CRP is a novel biomarker of coronary heart disease risk prediction. Type 2 diabetes mellitus individuals are at an increased risk of developing cardiovascular diseases. In this study, we aim to find the association of HbA1c with hs-CRP in the South Indian population. Materials and Methods: This was a retrospective study. The study participants were categorised into 3 groups based on their serum hs-CRP levels: Group 1 with hs-CRP &lt; 1.0 mg/L; group 2 with hs-CRP 1.0 – 3.0 mg/L and group 3 with hs-CRP &gt; 3.0 mg/L. The sample size was estimated to be 40 for each group. Age, fasting blood glucose, postprandial blood glucose, HbA1c, hs-CRP, total count, and ESR were collected. Statistical analyses were performed with SPSS version 16.0. Results: Age showed no significant difference among the groups. FBS, PPBS, HbA1c, TLC, and ESR showed a significant difference among the 3 groups. TLC, FBS, PPBS, and HbA1c had a significant positive correlation with hs-CRP. Conclusion: hs-CRP has a statistically significant positive correlation with HbA1c, fasting blood sugar, and postprandial blood sugar Additionally, we also observed that total leukocyte count had a significant positive correlation with hs-CRP and HbA1c. Hence, our results show that impaired glycemic control is associated with an increased risk of developing cardiovascular diseases.

Metabolite-assisted models improve risk prediction of coronary heart disease in patients with diabetes.

Background: Patients with diabetes have a two-to four-fold increased incidence of cardiovascular diseases compared with non-diabetics. Currently, there is no recognized model to predict the occurrence and progression of CVDs in diabetics. Objective: This work aimed to develop a metabolic biomarker-assisted model, a combination of metabolic markers with clinical variables, for risk prediction of CVDs in diabetics. Methods: A total of 475 patients with diabetes were studied. Each patient underwent coronary angiography. Plasma samples were analyzed by liquid chromatography-quadrupole time-of-flight mass spectrometry. Ordinal logistic regression and random forest were used to screen metabolites. Receiver operating characteristic (ROC) curve, nomogram, and decision curve analysis (DCA) were employed to evaluate their prediction performances. Results: Ordinal logistic regression screened out 34 differential metabolites (adjusted-false discovery rate p < 0.05) from 2059 ion features by comparisons of diabetics with and without CVDs. Random forest identified methylglutarylcarnitine and lysoPC (18:0) as the metabolic markers (mean decrease gini >1.0) for non-significant CVDs (nos-CVDs) versus normal coronary artery (NCA), 1,3-Octadiene and 3-Octanone for acute coronary syndrome (ACS) versus nos-CVDs, and lysoPC (18:0) for acute coronary syndrome versus normal coronary artery. For risk prediction, the metabolic marker-assisted models provided areas under the curve of 0.962-0.979 by ROC (0.576-0.779 for the base models), and c-indices of 0.8477-0.9537 by nomogram analysis (0.1514-0.5196 for the base models). Decision curve analysis (DCA) showed that the models produced greater benefits throughout a wide range of risk probabilities compared with the base model. Conclusion: Metabolic biomarker-assisted model remarkably improved risk prediction of cardiovascular disease in diabetics (>90%).

Heart disease risk prediction using deep learning techniques with feature augmentation

Cardiovascular diseases state as one of the greatest risks of death for the general population. Late detection in heart diseases highly conditions the chances of survival for patients. Age, sex, cholesterol level, sugar level, heart rate, among other factors, are known to have an influence on life-threatening heart problems, but, due to the high amount of variables, it is often difficult for an expert to evaluate each patient taking this information into account. In this manuscript, the authors propose using deep learning methods, combined with feature augmentation techniques for evaluating whether patients are at risk of suffering cardiovascular disease. The results of the proposed methods outperform other state of the art methods by 4.4%, leading to a precision of a 90%, which presents a significant improvement, even more so when it comes to an affliction that affects a large population.

Retracted: Implementation of a Heart Disease Risk Prediction Model Using Machine Learning.

[This retracts the article DOI: 10.1155/2022/6517716.].

A Survey on Cardiac Disease Classifying from Imbalanced Healthcare Facts Using Ensemble Classification Techniques

Abstract: Heart disease is the leading problem of death globally, and early detection is pivotal in preventing the progression of the disease. In this paper, Improved EnsembleSVM method is proposed for the prediction of heart disease risk. The technique involves randomly partitioning the dataset into smaller subsets using a mean based splitting approach. Oversampling of imbalanced data introduces the extension of Synthetic Minority Over-sampling Technique through a recent ideology, and recurrent ensemble-based noise filter called duplicative-Partitioning Filter, which can overwhelm the hindrance fashioned by noisy and frontier models in overbalanced dataset. Ensemble classifier with oversampling technique plays an accurate result for predict heart disease in efficient way.

Vendor independent coronary calcium scoring improves individual risk assessment – the Multi-Ethnic Study of Atherosclerosis (MESA)

Abstract Background Coronary artery calcium (CAC) scoring improves event prediction of coronary heart disease (CHD) and can be used to guide initiation or deferral of statin therapy in asymptomatic individuals at low-to-intermediate risk. However, there is substantial variation of CAC scores acquired with different CT scanners. Therefore, CAC scoring discrepancies may lead to suboptimal patients' treatment and harmonization is needed. Purpose The aim of our study was twofold. Our first aim was to develop a calibration tool resulting in vendor neutral Agatston Score (vnAS). Second, we aimed to assess the effect of using this calibration tool in the existing Multi-Ethnic Study of Atherosclerosis (MESA) study cohort on both risk prediction of coronary heart disease (CHD), and initiation of statin therapy. Methods Two static anthropomorphic phantoms containing multiple CAC inserts were imaged on seven different CT systems and one EBT system. For each CT system, the vnAS calculator parameters were derived using regression analysis based on Agatston scores from EBT and CT. To validate our vnAS, we used CAC scoring information and clinical data of participants from MESA study. All included participants (Cohort I, n=3181) were assigned to one out of four calcium groups, which were defined as zero-calcium (AS and vnAS of 0), low-calcium (AS and vnAS of 1–99) high-calcium (AS and vnAS ≥100), and reclassified individuals (AS &amp;lt;100 and vnAS ≥100). The occurrence of CHD events in each group was assessed and multivariable Cox regression models were used to assess the added value of the vnAS in the prediction of CHD events For a sub-cohort of 890 participants at intermediate cardiovascular risk, the potential benefit from statin therapy was estimated based on the number needed to treat (NNT). NNT were determined for patients with vnAS ≥100 and vnAS ≥300 with original AS below 100 (group I) or 300 (group II), respectively. Results For all CT systems, a high degree of correlation with EBT Agatston scores was shown (R2 &amp;gt;0.932). Using the vnAS, 85 individuals (2.7%) were reclassified from a lower to a higher risk category. For the reclassified participants, CHD event rates increased significantly from 7% to 15% (p=0.008) with a CHD hazard ratio of 3.39 (95% CI 1.82–6.35, p=0.001) (Figure 1). In intermediate risk cohort, the NNT was smaller for reclassified individuals as compared to their original (low) calcium group, at 7 vs 12 for group I and 2 vs 15 for group II, respectively. The summary of vnAS applicability is depicted in Figure 2. Conclusions We developed a calibration tool, which enables to calculate vendor neutral AS (vnAS). Based on vnAS, 85 individuals from MESA study, who were reclassified from a lower to a higher calcium category, did indeed have a higher CHD event rate. Consequently, the potential benefit from statin therapy, based on vnAS, was also increased for reclassified participants at intermediate cardiovascular risk. Funding Acknowledgement Type of funding sources: None.