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

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Jae Kwon Kim ◽  
Sanggil Kang
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Correlation Analysis ◽  
Risk Prediction ◽  
Roc Curve ◽  
Chd Risk ◽  
Proposed Model ◽  
Feature Correlation ◽  
Clinical Experiments

Background. Of the machine learning techniques used in predicting coronary heart disease (CHD), neural network (NN) is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA) using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC) curve of the proposed model (0.749 ± 0.010) was larger than the Framingham risk score (FRS) (0.393 ± 0.010). Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

scholarly journals An Optimized DBN-based Coronary Heart Disease Risk Prediction

2018 ◽  
Vol 13 (4) ◽  
pp. 492-502 ◽  
Author(s):  
Kahyun Lim ◽  
Byung Mun Lee ◽  
Ungu Kang ◽  
Youngho Lee
Keyword(s):  
Genetic Algorithm ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Risk Prediction ◽  
Disease Risk ◽  
World Health ◽  
Prediction Algorithm ◽  
Number Of Layers ◽  
Chd Risk ◽  
Better Than

Coronary Heart Disease (CHD) is the world’s leading cause of death according to a World Health Organization (WHO) report. Despite the evolution of modern medical technology, the mortality rate of CHD has increased. Nevertheless, patients often do not realize they have CHD until their condition is serious due to the complexity, high cost, and the side effects of the diagnosis process. Thus, research on predicting CHD risk has been conducted. The Framingham study is a widely-accepted study in this field. However, one of its limitations is its overestimation of risk, which threatens its accuracy. Therefore, this study suggests a more advanced CHD risk prediction algorithm based on Optimized-DBN (Deep Belief Network). Optimized- DBN is an algorithm to improve performance by overcoming the limitations of the existing DBN. DBN does not have the global optimum values for number of layers and nodes, which affects research results. We overcame this limitation by combining with a genetic algorithm. The result of genetic algorithm for deriving the number of layers and nodes of Optimized-DBN for CHD prediction was 2 layers, 5 and 7 nodes to each layers. The accuracy of the CHD prediction algorithm based on Optimized- DBN which is developed by applying results of genetic algorithm was 0.8924, which is better than Framingham’s 0.5015 and DBN’s 0.7507. In the case of specificity, Optimized-DBN based CHD prediction was 0.7440, which was slightly lower than 0.8208 of existing DBN, but better than Framingham’s 0.65. In the case of sensitivity, Optimized-DBN is 0.8549, which is better than Framingham 0.4429 and DBN 0.7468. AUC of suggesting algorithm was 0.762, which was much better than Framingham 0.547 and DBN 0.570.

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

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12259
Author(s):  
Qian Wang ◽  
Wenxing Li ◽  
Yongbin Wang ◽  
Huijun Li ◽  
Desheng Zhai ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Prediction Model ◽  
Risk Prediction ◽  
Prediction Models ◽  
Risk Prediction Model ◽  
Support Vector ◽  
High Morbidity ◽  
Rural Residents ◽  
Chd Risk

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.

scholarly journals AK098656: a new biomarker of coronary stenosis severity in hypertensive and coronary heart disease patients

2022 ◽  
Vol 14 (1) ◽  
Author(s):  
Xin Wang ◽  
Ya-li Wu ◽  
Yuan-yuan Zhang ◽  
Jing Ke ◽  
Zong-wei Wang ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Roc Curve ◽  
Coronary Stenosis ◽  
Area Under The Curve ◽  
Discrimination Ability ◽  
Chd Risk ◽  
Significant Difference ◽  
Stenosis Severity ◽  
Novel Target

Abstract Background AK098656 may be an adverse factor for coronary heart disease (CHD), especially in patients with hypertension. This study aimed to analyze the effect of AK098656 on CHD and CHD with various complications. Methods A total of 117 CHD patients and 27 healthy control subjects were enrolled in the study. Plasma AK098656 expression was determined using the quantitative real-time polymerase chain reaction. Student’s t-test was used to compare AK098656 expression levels in different groups. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to quantify the discrimination ability between CHD patients and health controls and between CHD and CHD + complications patients. The relationship between AK098656 and coronary stenosis was analyzed using Spearman’s correlation. Results AK098656 expression was remarkably higher in CHD patients than in healthy controls (P = 0.03). The ROC curve revealed an effective predictive AK098656 expression value for CHD risk, with an AUC of 0.656 (95% CI 0.501–0.809). Moreover, AK098656 expression was increased in CHD + complications patients compared to CHD patients alone (P = 0.005), especially in patients with hypertension (CHD + hHTN, P = 0.030). The ROC curve revealed a predictive AK098656 prognostic value for discriminating between CHD and CHD + hHTN patients, with an AUC of 0.666 (95% CI 0.528–0.805). There was no significant difference in AK098656 expression in CHD patients with diabetes mellitus compared to CHD patients alone. In addition, AK098656 expression in CHD patients was positively correlated with stenosis severity (R = 0.261, P = 0.006). Conclusion AK098656 expression was significantly increased in patients with CHD, especially those with hypertension, and its expression level was positively correlated with the degree of coronary stenosis. This implied that AK098656 may be a risk factor for CHD and can potentially be applied in clinical diagnosis or provide a novel target for treatment.

Abstract 5090: Impact of Adding a Single Allele in the 9p21 Locus to Traditional Risk Factors on Risk Classification for Coronary Heart Disease and Implications for Lipid-Modifying Therapy in the White Population of the Atherosclerosis Risk in Communities (ARIC) Study

Circulation ◽  
2008 ◽  
Vol 118 (suppl_18) ◽  
Author(s):  
Ariel Brautbar ◽  
Christie Ballantyne ◽  
Kim Lawson ◽  
Vijay Nambi ◽  
Lloyd Chambless ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Risk Prediction ◽  
White Population ◽  
Atherosclerosis Risk In Communities ◽  
Chd Risk ◽  
Atherosclerosis Risk ◽  
Traditional Risk Factors ◽  
Aric Study

Aim: A single nucleotide polymorphism on chromosome 9p21, rs10757274 (9p21 allele), has been shown to be a predictor of coronary heart disease (CHD) in whites. We evaluated if the addition of the 9p21 allele to traditional risk factors (TRF) improved CHD risk prediction in the white population of the Atherosclerosis Risk in Communities (ARIC) study, and whether changes in risk prediction will modify lipid therapy recommendation. Methods: Whites (n=10,004) in the ARIC study for whom the 9p21 genotype and TRF (age, gender, systolic blood pressure, total cholesterol, smoking, diabetes, HDL-C, and anti-hypertensive medication use) information was available were included. Using Cox proportional hazards models, the ARIC Cardiovascular Risk Score (ACRS) which is based on TRF was determined. The impact of adding the 9p21 allele to TRF with respect to the area under the curve (AUC) of a receiver operating characteristic (ROC) curve and then risk strata reclassification was determined. Results: The addition of 9p21 allele to TRF was associated with a hazard ratio (HR) of 1.25 (p<0.0001) and an increase in the AUC for incident CHD from 0.776 to 0.780 (Δ= 0.004, 95% CI=0.001, 0.008). The 9p21 allele’s greatest influence to the ACRS (Table ) was observed in the intermediate (5–10% 10-year CHD risk) and intermediate-high (10 –20% 10-year CHD risk) categories with 19.3% and 16.9% reclassified, respectively, which would impact therapy, as approximately 90% of these individuals had LDL-C >100 mg/dL. Table: Reclassification in the different ACRS categories after the addition of the 9p21 allele to the traditional risk factors

scholarly journals AK098656 – A New Biomarker of Coronary Stenosis Severity in Hypertensive and Coronary Heart Disease Patients

2021 ◽  
Author(s):  
Xin Wang ◽  
Ya-li Wu ◽  
Yuan-yuan Zhang ◽  
Jing Ke ◽  
Bao-yu Zhang ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Roc Curve ◽  
Coronary Stenosis ◽  
Area Under The Curve ◽  
Discrimination Ability ◽  
Chd Risk ◽  
Significant Difference ◽  
Stenosis Severity ◽  
Novel Target

Abstract Background: AK098656 may be an adverse factor for coronary heart disease (CHD), especially in patients with hypertension. This study aimed to analyze the effect of AK098656 on CHD and CHD with various complications.Methods: A total of 117 CHD patients and 27 healthy control subjects were enrolled in the study. Plasma AK098656 expression was determined using the quantitative real-time polymerase chain reaction. Student’s t-test was used to compare AK098656 expression levels in different groups. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to quantify the discrimination ability between CHD patients and health controls and between CHD and CHD+complications patients. The relationship between AK098656 and coronary stenosis was analyzed using Spearman’s correlation.Results: AK098656 expression was remarkably higher in CHD patients than in healthy controls (P=0.03). The ROC curve revealed an effective predictive AK098656 expression value for CHD risk, with an AUC of 0.656 (95% CI: 0.501–0.809). Moreover, AK098656 expression was increased in CHD+complications patients compared to CHD patients alone (P=0.005), especially in patients with hypertension (CHD+hHTN, P=0.030). The ROC curve revealed a predictive AK098656 prognostic value for discriminating between CHD and CHD+hHTN patients, with an AUC of 0.666 (95% CI: 0.528–0.805). There was no significant difference in AK098656 expression in CHD patients with diabetes mellitus compared to CHD patients alone. In addition, AK098656 expression in CHD patients was positively correlated with stenosis severity (R=0.261, P=0.006).Conclusion: AK098656 expression was significantly increased in patients with CHD, especially those with hypertension, and its expression level was positively correlated with the degree of coronary stenosis. This implied that AK098656 may be a risk factor for CHD and can potentially be applied in clinical diagnosis or provide a novel target for treatment.

scholarly journals Effect of AK098656 on association of hypertension with coronary stenosis severity in coronary heart disease patients

2020 ◽  
Author(s):  
Xin Wang ◽  
Ya-li Wu ◽  
Yuan-yuan Zhang ◽  
Jing Ke ◽  
Bao-yu Zhang ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Roc Curve ◽  
Coronary Stenosis ◽  
Area Under The Curve ◽  
Discrimination Ability ◽  
Chd Risk ◽  
Significant Difference ◽  
Stenosis Severity ◽  
Novel Target

Abstract Background: AK098656 may be an adverse factor for coronary heart disease (CHD), especially in patients with hypertension. This study aimed to analyze the effect of AK098656 on CHD and CHD with various complications.Methods: A total of 117 CHD patients and 27 healthy control subjects were enrolled in the study. Plasma AK098656 expression was determined using the quantitative real-time polymerase chain reaction. Student’s t-test was used to compare AK098656 expression levels in different groups. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to quantify the discrimination ability between CHD patients and health controls and between CHD and CHD+complications patients. The relationship between AK098656 and coronary stenosis was analyzed using Spearman’s correlation.Results: AK098656 expression was remarkably higher in CHD patients than in healthy controls (P=0.03). The ROC curve revealed an effective predictive AK098656 expression value for CHD risk, with an AUC of 0.656 (95% CI: 0.501–0.809). Moreover, AK098656 expression was increased in CHD+complications patients compared to CHD patients alone (P=0.005), especially in patients with hypertension (CHD+HHTN, P=0.030). The ROC curve revealed a predictive AK098656 prognostic value for discriminating between CHD and CHD+HHTN patients, with an AUC of 0.666 (95% CI: 0.528–0.805). There was no significant difference in AK098656 expression in CHD patients with diabetes mellitus compared to CHD patients alone. In addition, AK098656 expression in CHD patients was positively correlated with stenosis severity (R=0.261, P=0.006).Conclusion: AK098656 expression was significantly increased in patients with CHD, especially those with hypertension, and its expression level was positively correlated with the degree of coronary stenosis. This implied that AK098656 may be a risk factor for CHD and can potentially be applied in clinical diagnosis or provide a novel target for treatment.

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