Comparative Analysis of Machine Learning Models and Key Risk Factors in Advancing Predictive Analytics for Coronary Heart Disease Over a Decade
DOI:
https://doi.org/10.61173/fjx4w228Keywords:
Coronary heart disease, Risk factors, Machine learningAbstract
This study aimed to identify key risk factors for coronary heart disease (CHD) and assess the performance of various machine learning models in predicting 10-year CHD risk. We conducted an exploratory data analysis using data from the Framingham Heart Study, which included 4,238 participants and 15 potential risk factors, to understand the distribution of variables and relationships. We used Chi-square and Mann-Whitney U tests to identify significant associations between risk factors and coronary heart disease. Logistic regression, random forest and support vector machine (SVM) models were established and their prediction accuracy and area under receiver operating characteristic curve (AUC) were evaluated. The results showed that age, systolic blood pressure and history of hypertension were the most influential risk factors. Logistic regression accuracyand AUCwere the highest, better than random forest and SVM. This indicates that we can pay more attention to such factors as age, systolic blood pressure and history of hypertension in subsequent CHD studies, and mainly use logistic regression model to predict coronary heart disease and optimize it.
References
[1] Yang H, Garibaldi JM: A hybrid model for automatic identification of risk factors for heart disease. J Biomed Inform 2015, 58 Suppl(Suppl):S171-S182.
[2] Murphy SL, Xu J, Kochanek KD: Deaths: final data for 2010. Natl Vital Stat Rep 2013, 61(4):1-117.
[3] Benjamin EJ, Muntner P, Alonso A, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Das SR et al: Heart Disease and Stroke Statistics-2019 Update: A Report From the American Heart Association. Circulation 2019, 139(10):e56-e528.
[4] Cui H, Liu Q, Wu Y, Cao L: Cumulative triglyceride-glucose index is a risk for CVD: a prospective cohort study. Cardiovasc Diabetol 2022, 21(1):22.
[5] Zhao D, Liu J, Wang M, Zhang X, Zhou M: Epidemiology of cardiovascular disease in China: current features and implications. Nat Rev Cardiol 2019, 16(4):203-212.
[6] Brown JC, Gerhardt TE, Kwon E: Risk Factors for Coronary Artery Disease. In: StatPearls. edn. Treasure Island (FL) ineligible companies. Disclosure: Thomas Gerhardt declares no relevant financial relationships with ineligible companies. Disclosure: Edward Kwon declares no relevant financial relationships with ineligible companies.; 2024.
[7] Hajar R: Risk Factors for Coronary Artery Disease: Historical Perspectives. Heart Views 2017, 18(3):109-114.
[8] Houssein EH, Mohamed RE, Ali AA: Heart disease risk factors detection from electronic health records using advanced NLP and deep learning techniques. Sci Rep 2023, 13(1):7173.
[9] Leening MJG, Cook NR, Franco OH, Manson JE, Lakshminarayan K, LaMonte MJ, Leira EC, Robinson JG, Ridker PM, Paynter NP: Comparison of Cardiovascular Risk Factors for Coronary Heart Disease and Stroke Type in Women. J Am Heart Assoc 2018, 7(19):e007514.
[10] Sletten AC, Peterson LR, Schaffer JE: Manifestations and mechanisms of myocardial lipotoxicity in obesity. J Intern Med 2018, 284(5):478-491.
[11] Machate DJ, Figueiredo PS, Marcelino G, Guimaraes RCA, Hiane PA, Bogo D, Pinheiro VAZ, Oliveira LCS, Pott A: Fatty Acid Diets: Regulation of Gut Microbiota Composition and Obesity and Its Related Metabolic Dysbiosis. Int J Mol Sci 2020, 21(11).
[12] Oniszczenko W, Stanislawiak E: Association between sex and body mass index as mediated by temperament in a nonclinical adult sample. Eat Weight Disord 2019, 24(2):291- 298.
[13] Hassan CAU, Iqbal J, Irfan R, Hussain S, Algarni AD, Bukhari SSH, Alturki N, Ullah SS: Effectively Predicting the Presence of Coronary Heart Disease Using Machine Learning Classifiers. Sensors (Basel) 2022, 22(19).
[14] Narasimhan G, Victor A: Analysis of computational intelligence approaches for predicting disease severity in humans: Challenges and research guidelines. J Educ Health Promot 2023, 12:334.
[15] Krittanawong C, Virk HUH, Bangalore S, Wang Z, Johnson KW, Pinotti R, Zhang H, Kaplin S, Narasimhan B, Kitai T et al: Machine learning prediction in cardiovascular diseases: a metaanalysis. Sci Rep 2020, 10(1):16057.
[16] Juhola M, Joutsijoki H, Penttinen K, Shah D, Polonen RP, Aalto-Setala K: Data analytics for cardiac diseases. Comput Biol Med 2022, 142:105218.
[17] Rasmy L, Nigo M, Kannadath BS, Xie Z, Mao B, Patel K, Zhou Y, Zhang W, Ross A, Xu H et al: Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data. Lancet Digit Health 2022, 4(6):e415-e425.
[18] Liu M, Li S, Yuan H, Ong MEH, Ning Y, Xie F, Saffari SE, Shang Y, Volovici V, Chakraborty B et al: Handling missing values in healthcare data: A systematic review of deep learningbased imputation techniques. Artif Intell Med 2023, 142:102587.
[19] Dai N, Deng Y, Wang B: Association between human blood metabolome and the risk of hypertension. BMC Genom Data 2023, 24(1):79.
[20] Satoh M, Metoki H, Asayama K, Murakami T, Inoue R, Tsubota-Utsugi M, Matsuda A, Hirose T, Hara A, Obara T et al: Age-Related Trends in Home Blood Pressure, Home Pulse Rate, and Day-to-Day Blood Pressure and Pulse Rate Variability Based on Longitudinal Cohort Data: The Ohasama Study. J Am Heart Assoc 2019, 8(15):e012121.
[21] Ntineri A, Stergiou GS, Thijs L, Asayama K, Boggia J, Boubouchairopoulou N, Hozawa A, Imai Y, Johansson JK, Jula AM et al: Relationship between office and home blood pressure with increasing age: The International Database of HOme blood pressure in relation to Cardiovascular Outcome (IDHOCO). Hypertens Res 2016, 39(8):612-617.
[22] Castro-Porras LV, Rojas-Martinez R, Aguilar-Salinas CA, Bello-Chavolla OY, Becerril-Gutierrez C, Escamilla-Nunez C: Trends and age-period-cohort effects on hypertension mortality rates from 1998 to 2018 in Mexico. Sci Rep 2021, 11(1):17553.
[23] Hosseini M, Yousefifard M, Baikpour M, Rafei A, Fayaz M, Heshmat R, Koohpayehzadeh J, Asgari F, Etemad K, Gouya MM et al: Twenty-year dynamics of hypertension in Iranian adults: age, period, and cohort analysis. J Am Soc Hypertens Dean&Francis 2015, 9(12):925-934.
[24] Song DK, Hong YS, Sung YA, Lee H: The effect of menopause on cardiovascular risk factors according to body mass index in middle-aged Korean women. PLoS One 2023, 18(3):e0283393.
[25] Global Burden of Metabolic Risk Factors for Chronic Diseases C, Lu Y, Hajifathalian K, Ezzati M, Woodward M, Rimm EB, Danaei G: Metabolic mediators of the effects of body-mass index, overweight, and obesity on coronary heart disease and stroke: a pooled analysis of 97 prospective cohorts with 1.8 million participants. Lancet 2014, 383(9921):970-983.
[26] Rodgers JL, Jones J, Bolleddu SI, Vanthenapalli S, Rodgers LE, Shah K, Karia K, Panguluri SK: Cardiovascular Risks Associated with Gender and Aging. J Cardiovasc Dev Dis 2019, 6(2).
[27] Lee DH, Lee JH, Kim SY, Lee HY, Choi JY, Hong Y, Park SK, Ryu DR, Yang DH, Hwang SJ et al: Optimal blood pressure target in the elderly: rationale and design of the HOW to Optimize eLDerly systolic Blood Pressure (HOWOLD-BP) trial. Korean J Intern Med 2022, 37(5):1070-1081.
[28] Williamson JD, Supiano MA, Applegate WB, Berlowitz DR, Campbell RC, Chertow GM, Fine LJ, Haley WE, Hawfield AT, Ix JH et al: Intensive vs Standard Blood Pressure Control and Cardiovascular Disease Outcomes in Adults Aged >/=75 Years: A Randomized Clinical Trial. JAMA 2016, 315(24):2673-2682.
[29] Group SR, Lewis CE, Fine LJ, Beddhu S, Cheung AK, Cushman WC, Cutler JA, Evans GW, Johnson KC, Kitzman DW et al: Final Report of a Trial of Intensive versus Standard Blood- Pressure Control. N Engl J Med 2021, 384(20):1921-1930.
[30] Kjeldsen SE: Hypertension and cardiovascular risk: General aspects. Pharmacol Res 2018, 129:95-99.
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