Prediction of Coronary Heart Disease Using Discriminant Analysis Algorithm in Active Elderly Men

Child abuse/neglect risk assessment under field practice conditions: Tests of external and temporal validity and comparison with heart disease prediction

Background and objective: Previous studies have demonstrated that tissue Doppler echocardiography and two-dimensional speckle tracking echocardiography are minimally invasive imaging methods used to screen for coronary atherosclerotic heart disease. However, they are not highly sensitive and specific for patients with suspected heart disease presenting with normal ventricular wall motion or for patients with early coronary heart disease. The newly emerging three-dimensional longitudinal strain imaging technology can overcome these shortcomings and has become a relatively mature technique for quantitative assessment of myocardial function. The left ventricular global longitudinal peak strain (LVGLPS) measured by three-dimensional longitudinal strain imaging technology contributes to early diagnosis of high-risk coronary atherosclerotic heart disease. Design: Single-center, open-label, diagnostic trial. Methods: Three hundred older adult patients with suspected coronary atherosclerotic heart disease receiving treatment from January 2013 to December 2017 at the Department of Ultrasound Medicine, Taihe Hospital of China, are being included in this study. These patients will be divided into three groups: low-risk group (n = 100; ≥ 70* diameter stenosis in one or two branches of the right main coronary artery and the left circumflex artery), high-risk group (n = 100; ≥ 50* diameter stenosis in the left main coronary artery or ≥ 70* diameter stenosis in the left anterior descending branch), and control group (n = 100; Outcome measures: The primary outcome measure is the sensitivity of the LVGLPS for prediction of coronary atherosclerotic heart disease. The secondary outcome measures are: (1) the specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and accuracy rate of the LVGLPS for prediction of coronary atherosclerotic heart disease; (2) change in the LVGLPS; (3) change in conventional echocardiography parameters; and (4) change in the receiver operating characteristic curve for prediction of high-risk coronary atherosclerotic heart disease using the LVGLPS. Discussion: The findings from this study will help to confirm that three-dimensional longitudinal strain imaging technology is highly sensitive and specific for patients with abnormal coronary arteries with suspected coronary heart disease but who present with normal ventricular wall motion. The change in the LVGLPS contributes to early diagnosis of high-risk coronary atherosclerotic heart disease in older adult patients. This helps clinicians to diagnose early coronary heart disease and take timely strategies to avoid serious cardiovascular events as much as possible. Ethics and dissemination: This study will be performed in accordance with the Declaration of Helsinki. All patients are informed of the study protocol and procedure and provide written informed consent. This trial was approved by Taihe Hospital (Affiliated Hospital of Hubei University of Medicine) (approval number: ethics No. 2013(03)) in January 2013. Participant recruitment and data collection began in January 2013 and will continue through December 2017. Outcome measure analysis will be performed and the trial will be completed in January 2018. Results will be disseminated through presentations at scientific meetings and publications in peer-reviewed journals. Trial registration: This trial was registered with the Chinese the Clinical Trial Registry (registration number: ChiCTR-DDD-17012839).

The Prospective Cardiovascular Munster Trial (PROCAM Trial) [1] was started at the beginning of 1979. The aim of the study was to establish the incidence of risk factors for myocardial infarction in the population, to investigate the relationship between the risk factors, and to improve prediction and early diagnosis of coronary heart disease. To this end employees from the public and private sectors were examined for risk factors for coronary heart disease and were surveyed to find new cases of lethal and nonlethal myocardial infarctions for comparison with already stored data. One main approach was to investigate the significance of a detailed diagnostic analysis of the lipid metabolism for the prediction of coronary heart disease. This investigation involved a physician using standardised questionnaires to put questions dealing with the history of the participant, the family history, nicotine and alcohol consumption, physical activities and — in women — the use of oral contraceptives. Additionally the blood pressure, the body height, and the body weight were measured and an electrocardiogram was done. Blood samples were taken after participants had fasted for 12 h, to assess more than 20 laboratory parameters. All results were communicated to the family doctor. The participant was informed whether the findings were normal or whether a check-up at the family doctor was necessary. This was the case in 25% of the women and in 40% of the men.

Coronary heart disease is one of the biggest causes of death in developing countries. Civil servants (PNS) are a group of people who are susceptible to the disease because of their busy lives. Framingham risk score (FRS) can predict the occurrence of coronary heart disease in the next 10 years. The purpose of this study was to determine the prediction of coronary heart disease for the next 10 years in civil servants in Jember Regency. This type of research is descriptive observational with a cross sectional design. The data was taken from the medical records of patients with echelon II and III civil servants who did a medical sheck up at Dr. Soebandi Hospital in December 2013. Prediction of coronary heart disease for the next 10 years using the FRS method. From 63 medical record data, the prediction results of coronary heart disease in civil servants were 76.2% low risk, 17.5% moderate risk, and 6.3% high risk.
 Keywords: coronary heart disease, civil servants, framingham risk score

Objectives : This study was to research the relationship between Hwa-Byung and Framingham coronary risk score(FRS), cardiovascular disease. Methods : 649 people participated in the community based cohort study in Wonju City of South Korea from July 2nd to August 30th in 2006. Educated investigators checked up systolic & diastolic blood pressure and surveyed Hwa-Byung Diagnostic Interview Schedule(HBDIS), cohort questionnaire about gender, age, smoking, diabetes. Blood sample was collected from participants to analyze total cholesterol, HDL-cholesterol. FRS was calculated from collected data. 10-year prediction of coronary heart disease was determined from FRS by using score sheet that is estimated by Wilson et al. Collected data were analyzed by the chi-square test. Results : 1. Low risk number of people was 18(52.9%) in Hwa-Byung group, 263(42.8%) in non Hwa-Byung group. p-value was 0.472. Difference of the two group was invalid. 2. The number of people below or equal to average 10-year prediction of coronary heart disease as gnder & age, Hwa-Byung group was 19(55.9%), non Hwa-Byung group was 412(67.0%). p-value was 0.251. Difference of the two group was invalid. Conclusions : There was no correlationship Between Hwa-Byung and 10-year prediction of coronary heart disease.

This study aimed to optimize early coronary heart disease (CHD) prediction using a genetic algorithm (GA)-based convolutional neural network (CNN) feature engineering approach. We sought to overcome the limitations of traditional hyperparameter optimization techniques by leveraging a GA for superior predictive performance in CHD detection. Utilizing a GA for hyperparameter optimization, we navigated a complex combinatorial space to identify optimal configurations for a CNN model. We also employed information gain for feature selection optimization, transforming the CHD datasets into an image-like input for the CNN architecture. The efficacy of this method was benchmarked against traditional optimization strategies. The advanced GA-based CNN model outperformed traditional methods, achieving a substantial increase in accuracy. The optimized model delivered a promising accuracy range, with a peak of 85% in hyperparameter optimization and 100% accuracy when integrated with machine learning algorithms, namely naïve Bayes, support vector machine, decision tree, logistic regression, and random forest, for both binary and multiclass CHD prediction tasks. The integration of a GA into CNN feature engineering is a powerful technique for improving the accuracy of CHD predictions. This approach results in a high degree of predictive reliability and can significantly contribute to the field of AI-driven healthcare, with the possibility of clinical deployment for early CHD detection. Future work will focus on expanding the approach to encompass a wider set of CHD data and potential integration with wearable technology for continuous health monitoring.

Machine learning provides increasingly reliable assistance for medical experts in diagnosing coronary heart disease. This study proposes a deep learning hybrid model based coronary heart disease (CAD) prediction method, which can significantly improve the prediction accuracy compared to traditional solutions. This research scheme is based on the data of 7291 patients and proposes a hybrid model, which uses two different deep neural network models and a recurrent neural network model as the main model for training. The prediction results based on the main model training use a k-nearest neighbor model for secondary training so as to improve the accuracy of coronary heart disease prediction. The comparison between the model prediction results and the clinical diagnostic results shows that the prediction model has a prediction accuracy rate of 82.8%, a prediction precision rate of 87.08%, a prediction recall rate of 88.57%, a prediction F1-score of 87.82%, and an area under the curve value of 0.8 in the test set. Compared to single model machine learning predictions, the hybrid model has a significantly improved accuracy and has effectively solved the problem of overfitting. A deep learning based CAD prediction hybrid model that combines multiple weak models into a strong model can fully explore the complex inter-relationships between various features under limited feature values and sample size, improve the evaluation indicators of the prediction model, and provide effective auxiliary support for CAD diagnosis.

With completion of the human genome sequence, expectations are rising that a detailed catalogue will soon be available of all important common genetic susceptibility variants for human diseases, including coronary heart disease (CHD) and related atherosclerotic cardiovascular disease (CVD).1 In light of this seminal event in medical research, it is curious that the family history of CHD has not been accorded a more central role in risk prediction and disease prevention by clinicians and public health professionals. A positive family history of CHD is present in the majority of cases of premature-onset CHD.2 In cases of familial hypercholesterolemia and other rare forms of premature-onset CVD, CHD clearly segregates in a mendelian fashion. For most cases of premature-onset CHD, the mode of genetic transmission in families is less clear. Although the family history of CHD has been considered a putative risk factor for decades, it has not been incorporated along with other established risk factors such as hyperlipidemia, hypertension, and cigarette smoking in some widely applied multivariable risk algorithms,3 though other risk algorithms do incorporate family history information.4 See p 2150 This cautious approach to widespread application of family history information is not due to insufficient evidence. Risks for CHD death are greatest in monozygotic (identical) compared with dizygotic (nonidentical) twins, particularly when there is a premature (eg, <65 years) age of onset in the initially affected twin.5 In multiple prospective studies involving hundreds of thousands of men and women, a parental history of premature CHD is a significant risk factor for CVD even after multivariable adjustment. Relative risk estimates generally range from 1.2 to 2.0, as noted in the Physician’s Health Study and Women’s Health Study,6 although the estimated magnitude of risk associated with early-onset parental disease is substantially higher in some studies.7 …

ObjectivesThe importance of the prediction of coronary heart disease (CHD) has been recognized in Korea; however, few studies have been conducted in this area. Therefore, it is necessary to develop a method for the prediction and classification of CHD in Koreans.MethodsA model for CHD prediction must be designed according to rule-based guidelines. In this study, a fuzzy logic and decision tree (classification and regression tree [CART])-driven CHD prediction model was developed for Koreans. Datasets derived from the Korean National Health and Nutrition Examination Survey VI (KNHANES-VI) were utilized to generate the proposed model.ResultsThe rules were generated using a decision tree technique, and fuzzy logic was applied to overcome problems associated with uncertainty in CHD prediction.ConclusionsThe accuracy and receiver operating characteristic (ROC) curve values of the propose systems were 69.51% and 0.594, proving that the proposed methods were more efficient than other models.

This report was derived from a workshop on cardiovascular risk assessment sponsored by the National Heart, Lung, and Blood Institute, which addressed whether risk equations developed in the Framingham Heart Study (FHS) for predicting new-onset coronary heart disease (CHD) apply to diverse population groups. Preparation for the workshop included a reanalysis and comparison of prospective studies in several different populations in which risk factors were related to cardiovascular outcomes. Some studies included fatal and nonfatal CHD end points, whereas others contained only CHD mortality. Extensive collaboration provided as much uniformity as possible with respect to both risk factors and CHD end points. The FHS has led in defining the quantitative impact of risk factors.1 Many potential risk factors were measured and related to cardiovascular outcomes. Several risk factors proved to be strong, largely independent predictors of cardiovascular disease (CVD). These factors—advancing age, cigarette smoking, blood pressure (particularly systolic), cholesterol in total serum and HDL, and diabetes—served as the basis for the development of risk prediction equations.1 If FHS risk estimates are to be widely used, they must apply widely in the US population. To document their transportability, they must be compared with prospective studies in other populations. Although the FHS is the longest running prospective study, there are other major studies. The cardiovascular end points of these other studies have varied. Some include cardiovascular morbidity and mortality; others have only cardiovascular mortality. Among the end points, CHD is the most extensively reported; for this reason, CHD was the primary focus of the workshop. ### Multivariate Relative Risk Comparisons In preparation for the workshop, multivariate regression coefficients for each risk factor were compared in different populations with those of the FHS. Adjusted relative risk estimates make it possible to determine whether each independent risk factor confers a similar or different relative risk among different …

Homocysteine and metabolic syndrome: From clustering to additional utility in prediction of coronary heart disease

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.

The present article presents equations for the prediction of coronary heart disease (CHD) in a population with high rates of diabetes and albuminuria, derived from data collected in the Strong Heart Study, a longitudinal study of cardiovascular disease in 13 American Indian tribes and communities in Arizona, North and South Dakota, and Oklahoma. Participants of the Strong Heart Study were examined initially in 1989-1991 and were monitored with additional examinations and mortality and morbidity surveillance. CHD outcome data through December 2001 showed that age, gender, total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein cholesterol, smoking, diabetes, hypertension, and albuminuria were significant CHD risk factors. Hazard ratios for ages 65 to 75 years, hypertension, LDL cholesterol > or = 160 mg/dL, diabetes, and macroalbuminuria were 2.58, 2.01, 2.44, 1.66, and 2.11 in men and 2.03, 1.69, 2.17, 2.26, and 2.69 in women, compared with ages 45 to 54 years, normal blood pressure, LDL cholesterol <100 mg/dL, no diabetes, and no albuminuria. Prediction equations for CHD and a risk calculator were derived by gender with the use of Cox proportional hazards model and the significant risk factors. The equations provided good discrimination ability, as indicated by a c statistic of 0.70 for men and 0.73 for women. Results from bootstrapping methods indicated good internal validation and calibration. A "risk calculator" has been developed and placed on the Strong Heart Study Web site, which provides predicted risk of CHD in 10 years with input of these risk factors. This may be valuable for diverse populations with high rates of diabetes and albuminuria.

Risk Scores for Prediction of Coronary Heart Disease: An Update

Cardiovascular sickness is the largest cause of death in developing countries. The study of this coronary heart disease prediction model using a data mining technique and decision tree algorithm are applied in medical research, especially in heart disease prediction. The Coronary Heart Disease is also known as Coronary Artery Disease (CAD). Hence the decision system is analyses the heart disease for the patient. In this paper coronary illness studied a more number of input attributes and database records based on the patient's clinical data. This paper focuses on the around the prediction of heart disease accuracy value using the decision tree algorithm.