A database for using machine learning and data mining techniques for coronary artery disease diagnosis

Alizadehsani, Roohallah; Roshanzamir, Mohamad; Abdar, Moloud; Beykikhoshk, Adham; Khosravi, Abbas; Panahiazar, Maryam; Koohestani, Afsaneh; Khozeimeh, Fahime; Nahavandi, Saeid; Sarrafzadegan, Nizal

doi:10.1038/s41597-019-0206-3

Cited by 87 publications

(46 citation statements)

References 48 publications

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“…The gold standard for the diagnosis of coronary heart disease has been coronary angiography, which has high sensitivity and specificity. However, due to its complicated operation, high economic cost and invasive detection, many patients have low acceptance (3). At present, coronary stent implantation is a vital interventional method in treating coronary heart disease, which mainly improves myocardial blood supply by reconstructing narrow lumen, thus achieving the purpose of treatment (4).…”

Section: Introductionmentioning

confidence: 99%

lncRNA‑MALAT1 expression in patients with coronary atherosclerosis and its predictive value for in‑stent restenosis

Qiu

Sun

2020

Exp Ther Med

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This study was designed to investigate the long non-coding RNA (lncRNA)-metastasis associated lung adenocarcinoma transcript 1 (MALAT1) expression in patients with coronary atherosclerosis and its predictive value for in-stent restenosis. Ninety-five patients with coronary heart disease who came to our hospital for treatment and underwent stent implantation were selected as a research group (RG), and 95 volunteers undergoing physical examination who did not suffer from coronary heart disease during the same period were selected as a control group (CG). MALAT1 of subjects in both groups before and after treatment were detected by RT-qPCR, and N-terminal pro-brain natriuretic peptide (NT-proBNP), high sensitivity C-reactive protein (hs-CRP), lactate dehydrogenase (LDH), and creatine kinase isoenzyme (CK-MB) of them in the RG before treatment were detected. The level was evaluated and detected, and its correlation with MALAT1 was analyzed. Then, the predictive value of MALAT1 for in-stent restenosis in patients with coronary heart disease was analyzed. MALAT1 expression in patients with coronary heart disease was higher than that of normal subjects (P<0.05); after treatment, the expression levels of MALAT1, NT-proBNP, hs-CRP, LDH, and CK-MB in the serum of patients were significantly lower than those before treatment (P<0.05); MALAT1 expression was positively correlated with the expression levels of NT-proBNP, hs-CRP, LDH, and CK-MB (P<0.05). Receiver operating characteristic of MALAT1 for predicting in-stent restenosis in patients with coronary heart disease was over 0.8; the number of lesions, MALAT1, diabetes, NT-proBNP and hs-CRP were independent risk factors for in-stent restenosis. MALAT1 is highly expressed in the serum of patients with coronary heart disease, and it has high value in its diagnosis and the prediction of in-stent restenosis. It is also an independent risk factor for in-stent restenosis in patients with coronary heart disease.

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Section: Introductionmentioning

confidence: 99%

lncRNA‑MALAT1 expression in patients with coronary atherosclerosis and its predictive value for in‑stent restenosis

Qiu

Sun

2020

Exp Ther Med

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“…Ten-fold cross-validation was used on the extracted data to evaluate the performance of the algorithms. We used the accuracy, sensitivity, specificity, and area under the curve (AUC) of the algorithms for their performance comparison [54][55][56]. The accuracy of these algorithms is shown in Table 2.…”

Section: Resultsmentioning

confidence: 99%

Physical Fatigue Detection Using Entropy Analysis of Heart Rate Signals

et al. 2020

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Physical fatigue is one of the most important and highly prevalent occupational hazards in different industries. This research adopts a new analytical framework to detect workers’ physical fatigue using heart rate measurements. First, desired features are extracted from the heart signals using different entropies and statistical measures. Then, a feature selection method is used to rank features according to their role in classification. Finally, using some of the frequently used classification algorithms, physical fatigue is detected. The experimental results show that the proposed method has excellent performance in recognizing the physical fatigue. The achieved accuracy, sensitivity, and specificity rates for fatigue detection are 90.36%, 82.26%, and 96.2%, respectively. The proposed method provides an efficient tool for accurate and real-time monitoring of physical fatigue and aids to enhance workers’ safety and prevent accidents. It can be useful to develop warning systems against high levels of physical fatigue and design better resting times to improve workers’ safety. This research ultimately aids to improve social sustainability through minimizing work accidents and injuries arising from fatigue.

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“…Prediksi penyakit jantung adalah salah satu area yang tumbuh untuk prediksi tersebut. [1] [2] Kekurangan Dokter, ahli dan mengabaikan gejala pasien menyebabkan tantangan besar yang dapat menyebabkan kematian, cacat bagi pasien. Oleh karena itu diperlukan sistem pakar yang berfungsi sebagai alat analisis untuk menemukan informasi dan pola tersembunyi dalam data medis penyakit dengar.…”

Section: Pendahuluanunclassified

“…Dengan menggunakan data mining pada aplikasi orange bersama dengan metode eksplorasi lainnya. Dataset terdiri 14 atribut terdiri dari data nominal dan numerik, target kelas data set ini adalah tidak adanya (0) dan adanya penyakit jantung (1). Hasil perbandingan menunjukkan bahwa dalam penggunaan algoritma klasifikasi data mining yang digunakan yaitu Algoritma Naive Bayes, Random Forest, Neural Network dapat kita lihat bahwa algoritma Naive Bayes adalah algoritma yang tepat dan akurat digunakan untuk dapat melakukan prediksi penderita penyakit jantung dengan persentase sebesar 83 %.…”

Section: Kesimpulanunclassified

Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining

Derisma¹

2020

JAIC Polibatam

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Heart disease is a disease that contributes to a relatively high mortality rate. The rate of human death caused by disease in the heart is a widespread problem in the world. The main objective of this study is to predict people with heart disease using the publicly available dataset in the UCI Repository with the Heart Disease dataset. To obtain the best classification algorithm is by comparing three Algoritma Naive Bayes, Random Forest, Neural Network algorithms, which are frequently used to predict people with heart disease. Comparison results show that Naive Bayes ' algorithm is a precise and accurate algorithm used to predict people with heart disease with a percentage of 83 %.

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A database for using machine learning and data mining techniques for coronary artery disease diagnosis

Cited by 87 publications

References 48 publications

lncRNA‑MALAT1 expression in patients with coronary atherosclerosis and its predictive value for in‑stent restenosis

lncRNA‑MALAT1 expression in patients with coronary atherosclerosis and its predictive value for in‑stent restenosis

Physical Fatigue Detection Using Entropy Analysis of Heart Rate Signals

Perbandingan Kinerja Algoritma untuk Prediksi Penyakit Jantung dengan Teknik Data Mining

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