Coronary heart disease (CHD) is one of the deadliest diseases and a risk prediction model for cardiovascular conditions is needed. Due to the huge number of features that lead to heart problems, it is often difficult for an expert to evaluate these huge features into account. So, there is a need of appropriate feature selection for the given CHD dataset. For early CHD detection, deep learning modes (DL) show promising results in the existing studies.
OBJECTIVE:
This study aimed to develop a deep convolution neural network (CNN) model for classification with a selected number of efficient features using the LASSO (least absolute shrinkage and selection operator) technique. Also, aims to compare the model with similar studies and analyze the performance of the proposed model using accuracy measures.
METHODS:
The CHD dataset of NHANES (National Health and Nutritional Examination Survey) was examined with 49 features using LASSO technique. This research work is an attempt to apply an improved CNN model for the classification of the CHD dataset with huge features CNN model with feature extractor consists of a fully connected layer with two convolution 1D layers, and classifier part consists of two fully connected layers with SoftMax function was trained on this dataset. Metrics like accuracy recall, specificity, and ROC were used for the evaluation of the proposed model.
RESULTS:
The feature selection was performed by applying the LASSO model. The proposed CNN model achieved 99.36% accuracy, while previous studies model achieved over 80 to 92% accuracy.
CONCLUSION:
The application of the proposed CNN with the LASSO model for the classification of CHD can speed up the diagnosis of CHD and appears to be effective in predicting cardiovascular disease based on risk features.
MainiEVenkateswarluBMainiBMarwahaD. Machine learning-based heart disease prediction system for Indian population: An exploratory study done in South India. Med J Armed Forces India. 2021; 77(3): 302–311. Available from: doi: 10.1016/J.MJAFI.2020.10.013.
2.
SoniJAnsariUSharmaDSoniS. Predictive data mining for medical diagnosis: An overview of heart disease prediction. Int J Comput Appl. 2011; 17(8): 43–48. Available from: doi: 10.5120/2237-2860.
3.
DiwakarMTripathiAJoshiKMemoriaMSinghPKumarN. Latest trends on heart disease prediction using machine learning and image fusion. Mater Today: Proc. 2021; 37(Part 2): 3213–3218. Available from: doi: 10.1016/J.MATPR.2020.09.078.
4.
KondababuASiddharthaVKumarBBPenumutchiB. A comparative study on machine learning based heart disease prediction. Mater Today: Proc. 2021. Available from: doi: 10.1016/J.MATPR.2021.01.475.
5.
BenjaminEJ, et al. Heart Disease and Stroke Statistics – 2019 Update: A Report from the American Heart Association. American Heart Association. 2019; 139: 56–528. Available from: doi: 10.1161/CIR.0000000000000659.
6.
National Institutes of Health NIH: National Cholesterol Education Program ATP III Guidelinesr, United States National Institutes of Health. 2001.
7.
WulsinDFGuptaJRManiRBlancoJA. Modeling electroencephalography waveforms with semi-supervised deep belief nets fast classification and anomaly measurement. Journal of Neural Engineering. 2011; 8(3): 036015.
8.
KannelWBCastelliWPGordonTMcNamaraPM. Serum cholesterol, lipoproteins, and the risk of coronary heart disease. The Framingham study. Ann Intern Med. 1971; 74(1): 1–12. Available from: doi: 10.7326/0003-4819-74-1-1.
9.
StamlerJVaccaroONeatonJDWentworthD. Diabetes, other risk factors, and 12-yr cardiovascular mortality for men screened in the Multiple Risk Factor Intervention Trial. Diabetes Care. 1994; 16(2): 434–444. Available from: doi: 10.2337/diacare.16.2.434.
10.
VasanRSLarsonMGLeipEPEvansJCO’DonnellCJKannelWBLevyD. Impact of high-normal blood pressure on the risk of cardiovascular disease. N Engl J Med. 2001; 345(18): 1291–7. Available from: doi: 10.1056/NEJMoa003417.
11.
BurkeAPFarbAMalcomGTLiangYHSmialekJVirmaniR. Coronary risk factors and plaque morphology in men with coronary disease who died suddenly. N Engl J Med. 1997; 336(18): 1276–82. Available from: doi: 10.1056/NEJM199705013361802.
12.
CelermajerDSSorensenKEGeorgakopoulosDBullCThomasORobinsonJDeanfieldJE. Cigarette smoking is associated with dose-related and potentially reversible impairment of endothelium-dependent dilation in healthy young adults. Circulation. 1994; 88(5): 2149–55. Available from: doi: 10.1161/01.CIR.88.5.2149.
13.
ChobanianAVBakrisGLBlackHRCushmanWCGreenLAIzzoJrJLJonesDWMatersonBJOparilSWrightJrJTRoccellaEJ. National High Blood Pressure Education Program Coordinating Committee. Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension. 2003; 42(6): 1206–1252. Available from: doi: 10.1161/01.HYP.0000107251.49515.c2.
14.
AhmedMAYasmeenAAAwadallaHElmadhounWMNoorSKAlmobarakAO. Prevalence and trends of obesity among adult Sudanese individuals: Population based study. Diabetes & Metabolic Syndrome: Clinical Research & Reviews. 2017; 11(2): 963–967. Available from: doi: 10.1016/j.dsx.2017.07.023.
15.
FavaAPlastinoMCristianoDSpanòACristofaroSOpipariCChillàACasalinuovoFColicaCBartoloMDPirritanoDBoscoD. Insulin resistance possible risk factor for cognitive impairment in fibromyalgia patients. Metabolic Brain Disease. 2013; 28(4): 619–627. Available from: doi: 10.1007/s11011-013-9421-3.
16.
KiSKLeeSM. Voice Activity Detection based on DBN using the Likelihood Ratio. Journal of Rehabilitation Welfare Engineering & Assistive Technology. 2014; 8(3): 145–150.
17.
YanXChaoTTuKZhangY. Improving the prediction of human microRNA target genes by using ensemble algorithm. Federation of European Biochemical Societies. 2007; 581(8): 1586–1593.
18.
AlbertKFJohnRDivyangPSaleemTKevinMTCarolynJP, et al. Machine learning prediction of response to cardiac resynchronization therapy: Improvement versus current guidelines. Circ Arrhythmia Electrophysiol. 2019; 12(7). Available from: doi: 10.1161/CIRCEP.119.007316.
19.
KhajehaliNKhajehaliZTarokhMJ. The prediction of mortality influential variables in an intensive care unit: a case study. Personal Ubiquit Computm. 2021. Available from: doi: 10.1007/s00779-021-01540-5.
20.
KimYJSaqlianMLeeJY. Deep learning-based prediction model of occurrences of major adverse cardiac events during 1-year follow-up after hospital discharge in patients with AMI using knowledge mining. Personal Ubiquit Comput. 2022; 26(2): 259–267. Available from: doi: 10.1007/.
21.
OlsenCRMentzRJAnstromKJPageDPatelPA. Clinical applications of machine learning in the diagnosis, classification, and prediction of heart failure. Am Heart J. 2020; 229: 1–17. Available from: doi: 10.1016/J.AHJ.2020.07.009s00779-019-01248-7.
22.
AkbilgicOButlerLKarabayirIChangPKitzmanDAlonsoA, et al. Artificial intelligence applied to ecg improves heart failure prediction accuracy. J Am Coll Cardiol. 2021; 77(18): 3045. Available from: doi: 10.1016/S0735-1097(21)04400-4.
23.
SamuelOWYangBGengYAsogbonMGPirbhulalSMzurikwaoD, et al. A new technique for the prediction of heart failure risk driven by hierarchical neighborhood component-based learning and adaptive multi-layer. 2020.
24.
Fedesoriano Heart failure prediction dataset kaggle. Available from: https://www.kaggle.com/fedesoriano/heart-failure-prediction. Accessed 12 September 2022.
25.
Faiayaz WarisSKoteeswaranS. Heart disease early prediction using a novel machine learning method called improved K-means neighbor classifier in python. Mater Today: Proc. 2021. Available from: doi: 10.1016/J.MATPR.2021.01.570.
26.
MainiEVenkateswarluBGuptaA. Applying machine learning algorithms to develop a universal cardiovascular disease prediction system. Int. Conf. Intell. Data Commun. Technol. Internet Things. 2018. pp. 627–32.
27.
AbdulSUsmanMAliSAliSFaisalAMUllahN. A method for improving prediction of human heart disease using machine learning algorithms. Mobile Inf Syst. 2022: 1410169. 9 pages.
28.
AdlerEDVoorsAAKleinLMacheretFBraunOOUreyMA, et al. Improving risk prediction in heart failure using machine learning. Eur J Heart Fail. 2020; 22(1): 139–147. Available from: doi: 10.1002/EJHF.1628.
29.
PiresIMMarquesGGarciaNMPoncianoV. Machine learning for the evaluation of the presence of heart disease. Procedia Comput Sci. 2020; 177: 432–437. Available from: doi: 10.1016/J.PROCS.2020.10.058.
30.
AliMMPaulBKAhmedKBuiFMQuinnJMWMoniMA. Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Comput Biol Med. 2021; 136: 104672. Available from: doi: 10.1016/J.COMPBIOMED.2021.104672.
31.
Faiayaz WarisSKoteeswaranS. Heart disease early prediction using a novel machine learning method called improved K-means neighbor classifier in python. Mater Today: Proc. 2021. Available from: doi: 10.1016/J.MATPR.2021.01.570.
32.
KondababuASiddharthaVKumarBBPenumutchiB. A comparative study on machine learning based heart disease prediction. Mater Today: Proc. 2021. Available from: doi: 10.1016/J.MATPR.2021.01.475.
33.
JanMAwanAAKhalidMSNisarS. Ensemble approach for developing a smart heart disease prediction system using classification algorithms. Res Rep Clin Cardiol. 2018; 9: 33–45. Available from: doi: 10.2147/RRCC.S172035.
34.
Least Absolute Shrinkage and Selection Operator (LASSO). Available from: https://www.publichealth.columbia.edu/research/population-health-methods/least-absolute-shrinkage-and-selection-operator-lasso.
35.
NHANES (National Health and Nutritional Examination Survey). Available from: https://wwwn.cdc.gov/nchs/nhanes/.
36.
RabbiMFUddinMPAliMAKibriaMFAfjalMIIslamMSNituAM. Performance evaluation of data mining classification techniques for heart disease prediction. Am. J. Eng. Res. 2018; 7: 278–283.
37.
QrenawiMIAl SarrajW. Identification of cardiovascular diseases risk factors among diabetes patients using ontological data mining techniques. In Proceedings of the 2018 International Conference on Promising Electronic Technologies (ICPET), Deir El-Balah, Palestine, 3–4 October 2018. pp. 129–134.
38.
MoreKRaihanMMoreAPaduleSMondalS. A12176 Smart phone based “heart attack” risk prediction; innovation of clinical and social approach for preventive cardiac health. J. Hypertens. 2018; 36: e321.
39.
XuWYuKYeJLiHChenJYinFXuJZhuJLiDShuQ. Automatic pediatric congenital heart disease classification based on heart sound signal. Artif. Intell. Med. 2022; 126: 102257.
40.
AniruddhaDTamalBMeheliBScottT. Acton, An efficient convolutional neural network for coronary heart disease prediction. Expert Systems with Applications. 2020; 159: 113408, ISSN 0957-417. doi: 10.1016/j.eswa.2020.113408.
41.
AroojSRehmanSuImranAAlmuhaimeedAAlzahraniAKAlzahraniA. A deep-learning-based approach for the early detection of heart disease. Biomedicines. 2022; 10(11): 2796. doi: 10.3390/biomedicines10112796.
42.
AliLRahmanAKhanAZhouMJaveedAKhanJ. An Automated Diagnostic System for Heart Disease Prediction Based on Statistical Model and Optimally Configured Deep Neural Network. IEEE Access. 2019. pp. 1-1. doi: 10.1109/ACCESS.2019.2904800.
43.
AliLNiamatAKhanJAmiri GolilarzNXingzhongXNoorANourRBukhariSAC. An optimized stacked support vector machines based expert system for the effective prediction of heart failure. IEEE Access. 2019; 7: 54007–54014. doi: 10.1109/ACCESS.2019.2909969.
44.
EdinBLejlaGPZijoBNedimBMirzaDDenisMMesudJNaimVAlmirB. From heart murmur to echocardiography – congenital heart defects diagnostics using machine-learning algorithms. Medicina Academica Mostariensia. 2021; 9(2): 236–246.
45.
StokesKCastaldoRFranzeseMSalvatoreMFicoGGurbeta PokvicLBadnjevicAPecchiaL. A machine learning model for supporting symptom-based referral and diagnosis of bronchitis and pneumonia in limited resource settings. Biocybernetics and Biomedical Engineering. 2021; 41. doi: 10.1016/j.bbe.2021.09.002.
46.
AlicBGurbeta PokvicLBadnjevicA. Machine Learning Techniques for Classification of Diabetes and Cardiovascular Diseases. 2017. doi: 10.1109/MECO.2017.7977152.
