hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm

Tayefi, Maryam; Tajfard, Mohammad; Saffar, Sara; Hanachi, Parichehr; Amirabadizadeh, Alireza; Esmaeily, Habibollah; Taghipour, Ali; Ferns, Gordon A.; Moohebati, Mohsen; Ghayour‐Mobarhan, Majid

doi:10.1016/j.cmpb.2017.02.001

Cited by 119 publications

(73 citation statements)

References 20 publications

(19 reference statements)

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“…High-sensitivity (hs)-CRP is a more sensitive test for subtle inflammation, and serum hs-CRP levels may reflect the inflammatory state of various diseases. There have been many studies evaluating disease risk using serum hs-CRP, and several studies using serum hs-CRP have shown that cardiovascular disease and diabetes are positively correlated with systemic inflammation during disease progression [25–27]. Elevated serum CRP levels have been associated with obesity and systemic inflammation in MetS [4].…”

Section: Introductionmentioning

confidence: 99%

Associations among inflammation, mental health, and quality of life in adults with metabolic syndrome

Kim

Song

2018

Diabetol Metab Syndr

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BackgroundMetabolic syndrome (MetS), a pro-inflammatory state, has become increasingly common worldwide and is a major risk factor for type 2 diabetes mellitus and cardiovascular disease. Recently, studies on the relationships among inflammation, mental health, quality of life, and other diseases have been conducted.MethodsWe investigated the relationship between serum high-sensitivity C-reactive protein (hs-CRP) levels, as an indicator of inflammation, and the quality of life and psychiatric symptoms of Korean adults with MetS. The analysis used data from the Korean National Health and Nutrition Examination Survey, a cross-sectional survey of Korean civilians conducted from January to December 2015. Data from 1600 participants were analyzed in this study. Quality of life was assessed using the EuroQol 5-dimension (EQ-5D) instrument.ResultsSerum hs-CRP levels showed a significant inverse correlation with the EQ-5D index of the overall quality of life. High serum hs-CRP levels were positively associated with mobility problems and suicidal ideation in adults with MetS (multivariate-adjusted odds ratio [OR] 1.66, 95% confidence interval [CI] 1.03–2.66, p = 0.036; and multivariate-adjusted OR 2.48, 95% CI 1.23–4.99, p = 0.011).ConclusionsThese findings suggest that the elevated inflammatory status in MetS is associated with decreased quality of life and mental health problems. Further prospective studies are needed to confirm the impact of inflammation on the quality of life and mental health of adults with MetS.

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Section: Introductionmentioning

confidence: 99%

Associations among inflammation, mental health, and quality of life in adults with metabolic syndrome

Kim

Song

2018

Diabetol Metab Syndr

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“…The comparative analysis scheme is adopted from [1]. Furthermore, the accuracy performance is compared with state-of-art techniques such as adaptive fuzzy [5], Hybrid SVR [6], Rule mining with POA [7], Modified PSO [8], Re-RX with J48graft [9], Re-RX with C4.5 [9], QFAM-GA [10] and DNN-SAE [11].…”

Section: (A)mentioning

confidence: 99%

“…In the medical field, the pattern recognition and data mining play pivotal role to predict the diseases. Generally, researchers have focused on the classification based data mining schemes for early prediction of different types of diseases such as breast cancer [4], heart disease [5], Liver disorder [6], Thyroid [7], Lymphography [8], Parkinson's [9] and diabetes [10] etc. Significant amount of works have been carried out in these fields.…”

Section: Introductionmentioning

confidence: 99%

ENN Ensemble based Neural Network method for Diabetes Classification

Kumari,

2020

IJEAT

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Diabetes is considered as one of the most chronic disease which has serious impact on human health and leading cause of mortality worldwide. The early prediction of diabetes can help clinicians to provide a better diagnosis to the patients. Recently, computed aided diagnosis systems have gained attention due to significant growth in data mining, and machine learning. Several approaches are present based on the machine learning techniques but due to poor classification performance and computational complexity, it becomes difficult to utilize for real-time applications. Ensemble classification approaches have reported a noteworthy improvement in diabetes classification but desired accuracy is still a challenging task. Hence, in this work we introduce a combined hybrid approach called as ENN-Ensemble based neural network approach for diabetes classification. In this approach, a feature selection process is presented using neighboring search technique; the selected features are processed through the feature ranking model to generate the efficient feature subset for better classification accuracy. Finally, these features are learned and classified using neural network classifier. The experimental study shows that the proposed approach achieves better accuracy when compared with the existing techniques.

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“…In [10] decision tree used for data mining in heart disease. One of the objectives of this research is to extract the hidden knowledge from huge datasets of heart disease in order to create a predictor model for heart disease using decision tree.…”

Section: Related Workmentioning

confidence: 99%

New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection

et al. 2019

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The number and size of medical databases are rapidly increasing, and the advanced models of data mining techniques could help physicians to make efficient and applicable decisions. The challenges of heart disease data include the feature selection, the number of the samples; imbalance of the samples, lack of magnitude for some features, etc. This study mainly focuses on the feature selection improvement and decreasing the numbers of the features. In this study, imperialist competitive algorithm with meta-heuristic approach is suggested in order to select prominent features of the heart disease. This algorithm can provide a more optimal response for feature selection toward genetic in compare with other optimization algorithms. Also, the K-nearest neighbor algorithm is used for the classification. Evaluation result shows that by using the proposed algorithm, the accuracy of feature selection technique has been improved.

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hs-CRP is strongly associated with coronary heart disease (CHD): A data mining approach using decision tree algorithm

Abstract: Our model appears to be an accurate, specific and sensitive model for identifying the presence of CHD, but will require validation in prospective studies.

Cited by 119 publications

References 20 publications

Associations among inflammation, mental health, and quality of life in adults with metabolic syndrome

Associations among inflammation, mental health, and quality of life in adults with metabolic syndrome

ENN Ensemble based Neural Network method for Diabetes Classification

New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection

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