An improved machine learning-based prediction framework for early detection of events in heart failure patients using mHealth

Kaptoge S, Pennells L, De Bacquer D, Cooney MT, Kavousi M, Stevens G, Riley LM, Savin S, Khan T, Altay S, Amouyel P. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Health. 2019;7(10):e1332–45.

Article  Google Scholar 

Amini M, Zayeri F, Salehi M. Trend analysis of cardiovascular disease mortality, incidence, and mortality-to-incidence ratio: results from global burden of disease study 2017. BMC Public Health. 2021;21(1):1–12.

Article  Google Scholar 

Ritchie H, Spooner F, Roser M. Causes of death. Our world in data. 2018. Published online. https://ourworldindata.org/causes-of-death

Hosain N, Amin F, Maruf MF, Chowdhury MAQ, Chowdhury MR, Mahmud AU, Akter T, Anisuzzaman M, Rahim A. Global geographical discrepancy in numerical distribution of cardiovascular surgeries and human resource development in South Asia. JTCVS open. 2022;11:192–9.

Kumar AS, Sinha N. Cardiovascular disease in India: a 360 degree overview. Med J Armed Forces India. 2020;76(1):1.

Article  MathSciNet  Google Scholar 

Huffman MD, Prabhakaran D, Osmond C, Fall CH, Tandon N, Lakshmy R, Ramji S, Khalil A, Gera T, Prabhakaran P, Biswas SD. Incidence of cardiovascular risk factors in an Indian urban cohort: results from the New Delhi Birth Cohort. J Am Coll Cardiol. 2011;57(17):1765–74.

Article  PubMed  PubMed Central  Google Scholar 

Verma M, Jagia P, Roy A, Chaturvedi PK, Kumar S, Seth S, Singh V, Ojha V, Pandey NN. Lung water estimation on cardiac magnetic resonance imaging for predicting adverse cardiovascular outcomes in patients with heart failure. Br J Radiol. 2023;96(1146):20220723.

Article  PubMed  Google Scholar 

Sadoughi F, Khodaveisi T, Ahmadi H. The used theories for the adoption of electronic health record: a systematic literature review. Heal Technol. 2019;9:383–400.

Article  Google Scholar 

Liaw ST, Georgiou A, Marin H. Evaluation of Digital Health & Information Technology in Primary Care. Int J Med Informatics. 2020;144:104285–104285.

Article  Google Scholar 

Katarya R, Meena SK. Machine learning techniques for heart disease prediction: a comparative study and analysis. Heal Technol. 2021;11:87–97.

Article  Google Scholar 

Mohan S, Thirumalai C, Srivastava G. Effective heart disease prediction using hybrid machine learning techniques. IEEE access. 2019;7:81542–54.

Article  Google Scholar 

Jagadeeswari V, Subramaniyaswamy V, Logesh R, Vijayakumar V. A study on medical Internet of Things and Big Data in personalized healthcare system. Health Inf Sci Syst. 2018;6:1–20.

Article  Google Scholar 

Anuragi A, Sisodia DS, Pachori RB. Classification of focal and non-focal EEG signals using optimal geometrical features derived from a second-order difference plot of FBSE-EWT rhythms. Artif Intell Med. 2023;139: 102542.

Article  PubMed  Google Scholar 

Nourmohammadi-Khiarak J, Feizi-Derakhshi MR, Behrouzi K, Mazaheri S, Zamani-Harghalani Y, Tayebi RM. New hybrid method for heart disease diagnosis utilizing optimization algorithm in feature selection. Heal Technol. 2020;10:667–78.

Article  Google Scholar 

Sakamoto T, TEI, C., MURAYAMA, M., ICHIYASU, H., HADA, Y., HAYASHI, T. and AMANO, K. Giant T wave inversion as a manifestation of asymmetrical apical hypertrophy (AAH) of the left ventricle echocardiographic and ultrasono-cardiotomographic study. Jpn Heart J. 1976;17(5):611–29.

Article  CAS  PubMed  Google Scholar 

George B, Seals S, Aban I. Survival analysis and regression models. J Nucl Cardiol. 2014;21(4):686–94.

Article  PubMed  PubMed Central  Google Scholar 

Marx V. The big challenges of big data. Nature. 2013;498(7453):255–60.

Article  CAS  PubMed  Google Scholar 

Saheb T. An empirical investigation of the adoption of mobile health applications: integrating big data and social media services. Heal Technol. 2020;10(5):1063–77.

Article  Google Scholar 

Athilingam P, Jenkins B. Mobile phone apps to support heart failure self-care management: integrative review. JMIR cardio. 2018;2(1): e10057.

Article  PubMed  PubMed Central  Google Scholar 

Cruz-Ramos NA, Alor-Hernández G, Colombo-Mendoza LO, Sánchez-Cervantes JL, Rodríguez-Mazahua L, Guarneros-Nolasco LR. February MHealth apps for self-management of cardiovascular diseases: A scoping review. In Healthcare, vol. 10, No. 2. MDPI; 2022. p. 322.

Google Scholar 

Nikolaou K, Alkadhi H, Bamberg F, Leschka S, Wintersperger BJ. MRI and CT in the diagnosis of coronary artery disease: indications and applications. Insights Imaging. 2011;2(1):9–24.

Article  PubMed  Google Scholar 

Muhammad Y, Tahir M, Hayat M, Chong KT. Early and accurate detection and diagnosis of heart disease using intelligent computational model. Sci Rep. 2020;10(1):1–17.

Article  CAS  Google Scholar 

Javaid M, Haleem A, Singh RP, Suman R, Rab S. Significance of machine learning in healthcare: features, pillars and applications. Int J Intell Netw. 2022;9:58–73. https://doi.org/10.1016/j.ijin.2022.05.002.

Article  Google Scholar 

Shah A, Ahirrao S, Pandya S, Kotecha K, Rathod S. Smart cardiac framework for an early detection of cardiac arrest condition and risk. Front Public Health. 2021;9:762303. https://doi.org/10.3389/fpubh.2021.762303.

Article  PubMed  PubMed Central  Google Scholar 

Buttar HS, Li T, Ravi N. Prevention of cardiovascular diseases: role of exercise, dietary interventions, obesity and smoking cessation. Exp Clin Cardiol. 2005;10(4):229–49.

CAS  PubMed  PubMed Central  Google Scholar 

Yang G, Ren Y, Pan Q, Ning G, Gong S, Cai G, Zhang Z, Li L, Yan J. October. A heart failure diagnosis model based on support vector machine. In 2010 3rd international conference on biomedical engineering and informatics. IEEE. 2010;3:1105–1108. https://doi.org/10.1109/BMEI.2010.5639619.

Gharehchopogh FS, Khalifelu ZA. July Neural network application in diagnosis of patient: a case study. In International Conference on Computer Networks and Information Technology. IEEE; 2011. p. 245–9.

Google Scholar 

Alizadehsani R, Habibi J, Sani ZA, Mashayekhi H, Boghrati R, Ghandeharioun A, Khozeimeh F, Alizadeh-Sani F. Diagnosing coronary artery disease via data mining algorithms by considering laboratory and echocardiography features. Research in Cardiovascular Medicine. 2013;2(3):133.

Article  PubMed  PubMed Central  Google Scholar 

Tama BA, Im S, Lee S. Improving an intelligent detection system for coronary heart disease using a two-tier classifier ensemble. Biomed Res Int. 2020; 2020; Article ID 9816142. https://doi.org/10.1155/2020/9816142.

Parthiban G, Srivatsa SK. Applying machine learning methods in diagnosing heart disease for diabetic patients. Int J Appl Inf Syst. 2012;3(7):25–30.

Google Scholar 

Hernandez AF, Greiner MA, Fonarow GC, Hammill BG, Heidenreich PA, Yancy CW, Peterson ED, Curtis LH. Relationship between early physician follow-up and 30-day readmission among Medicare beneficiaries hospitalized for heart failure. JAMA. 2010;303(17):1716–22.

Article  CAS  PubMed  Google Scholar 

Shah NH, Milstein A, Bagley SC. Making machine learning models clinically useful. JAMA. 2019;322(14):1351–2.

Article  PubMed  Google Scholar 

Gerke S, Minssen T, Cohen G. Ethical and legal challenges of artificial intelligence-driven healthcare. In Artificial intelligence in healthcare. Academic Press; 2020. pp. 295–336. https://doi.org/10.1016/B978-0-12-818438-7.00012-5.

Tan M, Tsang IW, Wang L. Towards ultrahigh dimensional feature selection for big data. J Mach Learn Res. 2014;15:1371–429.

MathSciNet  Google Scholar 

Leevy JL, Khoshgoftaar TM, Bauder RA, Seliya N. A survey on addressing high-class imbalance in big data. J Big Data. 2018;5(1):1–30.

Article  Google Scholar 

Richhariya B, Tanveer M, Rashid AH, Alzheimer’s Disease Neuroimaging Initiative. Diagnosis of Alzheimer’s disease using universum support vector machine based recursive feature elimination (USVM-RFE). Biomed Signal Process Control. 2020;59:101903. https://doi.org/10.1016/j.bspc.2020.101903.

Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Mach Learn. 2002;46:389–422. https://doi.org/10.1023/A:1012487302797.

Yin Z, Zhang J. Operator functional state classification using least-square support vector machine based recursive feature elimination technique. Comput Methods Programs Biomed. 2014;113(1):101–15.

Article  PubMed  Google Scholar 

Mayerich DM, Walsh M, Kadjacsy-Balla A, Mittal S, Bhargava R. March Breast histopathology using random decision forests-based classification of infrared spectroscopic imaging data. In: In Medical Imaging 2014: Digital Pathology. 2014;9041:38–44. https://doi.org/10.1117/12.2043783.

Maniruzzaman M, Rahman MJ, Ahammed B, Abedin MM. Classification and prediction of diabetes disease using machine learning paradigm. Health Inf Sci Syst. 2020;8(7):1–14.

Google Scholar 

Masetic Z, Subasi A. Congestive heart failure detection using random forest classifier. Comput Methods Programs Biomed. 2016;130:54–64.

Article  PubMed  Google Scholar 

Kim HC, Ghahramani Z. Bayesian Gaussian process classification with the EM-EP algorithm. IEEE Trans Pattern Anal Mach Intell. 2006;28(12):1948–59.

Article  PubMed  Google Scholar 

Son J, Jung I, Park K, Han B. Trackingmentation with online gradient boosting decision tree. In: In Proceedings of the IEEE international conference on computer vision. 2015. p. 3056–64.

Google Scholar 

Abu Alfeilat HA, Hassanat AB, Lasassmeh O, Tarawneh AS, Alhasanat MB, Eyal Salman HS, Prasath VS. Effects of distance measure choice on k-nearest neighbor classifier performance: a review. Big data. 2019;7(4):221–48.

Article  PubMed  Google Scholar 

Sharma D, Kumar R, Jain A. Breast cancer prediction based on neural networks and extra tree classifier using feature ensemble learning. In: Measurement: Sensors, 24. 2022. p. 100560.

Google Scholar 

Jahromi AH, Taheri M. October. A non-parametric mixture of Gaussian naive Bayes classifiers based on local independent features. In: In 2017 Artificial intelligence and signal processing conference (AISP). IEEE; 2017. p. 209–12.

Google Scholar 

Wan S, Liang Y, Zhang Y, Guizani M. Deep multi-layer perceptron classifier for behavior analysis to estimate Parkinson’s disease severity using smartphones. IEEE Access. 2018;6:36825–33.

Article  Google Scholar 

Keerthiveena B, Esakkirajan S, Subudhi BN, Veerakumar T. A hybrid BPSO-SVM for feature selection and classification of ocular health. IET Image Proc. 2021;15(2):542–55.

Article  Google Scholar 

Lehrke M, Marx N. Diabetes Mellitus and Heart Failure. In: The American Journal of Cardiology. 2017. p. S37–47.

Google Scholar 

Böhm M, Swedberg K, Komajda M, Borer JS, Ford I, Dubost-Brama A, Lerebours G, Tavazzi L. Heart rate as a risk factor in chronic heart failure (SHIFT): the association between heart rate and outcomes in a randomised placebo-controlled trial. The Lancet. 2010;376(9744):886–94.

Article  Google Scholar 

Stamler J, Neaton JD, Wentworth DN. Blood pressure (systolic and diastolic) and risk of fatal coronary heart disease. Hypertension. 1989;13(5_supplement):I2.

Article  CAS 

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