• Sri Sumarlinda Universiti Kuala Lumpur Malaysia
  • Azizah Binti Rahmat Universiti Kuala Lumpur Malaysia
  • Zalizah Binti Awang Long Universiti Kuala Lumpur Malaysia



non-clinical factor risk, heart disease and stroke, statistical method, normalization test, validity and reliability test


Heart disease and stroke are the main contributions to health disorder. Two factors influence the susceptibility of the disease, namely clinical risk factors and non-clinical factors. This study aims to analyze the effect of non-clinical risk factors on the susceptibility to heart disease and stroke. The non-clinical risk factors are stress management, age, obesity, genetics, smoking, gender, lifestyle (nutrition), lifestyle (timing rest), and physical activity. Analysis of the influence of non-clinical risk factors on susceptibility using statistical methods, namely descriptive statistics, normalization tests with the Kolmogorov-Smirnov test, validity tests, reliability tests, and correlation tests. The descriptive statistical tests show that the risk factors for stress management, obesity, and smoking have a more significant influence than others. While Gender and physical activity have more negligible effect than others. Testing with one sample Kolmogorov-Smirnov Test shows that the data is normally distributed. Validity testing produces 100% valid data. The reliability test using Cronbach's alpha of 9 non-clinical risk factor items resulted in a value of 0.684, which means reliable. Correlation test between 9 items of non-clinical risk factor shows significant between items.


F. Mai, R. Del Pinto, and C. Ferri, “COVID-19 and cardiovascular diseases,” J. Cardiol., vol. 76, no. 5, pp. 453–458, 2020, doi: 10.1016/j.jjcc.2020.07.013.

J. Liu, C. Li, J. Xu, and H. Wu, “A patient-oriented clinical decision support system for CRC risk assessment and preventative care,” BMC Med. Inform. Decis. Mak., vol. 18, no. Suppl 5, 2018, doi: 10.1186/s12911-018-0691-x.

C. Chang, C. Huang, Y. Chou, J. Chang, and C. Sun, “Association Between Age‐Related Macular Degeneration and Risk of Heart Failure: A Population‐Based Nested Case‐Control Study,” J. Am. Heart Assoc., pp. 1–8, 2021, doi: 10.1161/jaha.120.020071.

M. El‐Chouli et al., “Time Trends in Simple Congenital Heart Disease Over 39 Years: A Danish Nationwide Study,” J. Am. Heart Assoc., vol. 10, no. 14, 2021, doi: 10.1161/jaha.120.020375.

J. Siddique et al., “Relative‐Intensity Physical Activity and Its Association With Cardiometabolic Disease,” J. Am. Heart Assoc., vol. 10, no. 14, pp. 1–3, 2021, doi: 10.1161/jaha.120.019174.

K. S. Zachrison et al., “Strategy for reliable identification of ischaemic stroke, thrombolytics and thrombectomy in large administrative databases,” Stroke Vasc. Neurol., vol. 6, no. 2, pp. 194–200, 2021, doi: 10.1136/svn-2020-000533.

J. Yang, L. Xiao, and K. Li, “Modelling clinical experience data as an evidence for patient-oriented decision support,” BMC Med. Inform. Decis. Mak., vol. 20, no. Suppl 3, pp. 1–11, 2020, doi: 10.1186/s12911-020-1121-4.

M. J. Hayat, A. Powell, T. Johnson, and B. L. Cadwell, “Statistical methods used in the public health literature and implications for training of public health professionals,” PLoS One, vol. 12, no. 6, pp. 1–10, 2017, doi: 10.1371/journal.pone.0179032.

M. Amini, F. Zayeri, and M. Salehi, “Trend analysis of cardiovascular disease mortality, incidence, and mortality-to-incidence ratio: results from global burden of disease study 2017,” BMC Public Health, vol. 21, no. 1, pp. 1–12, 2021, doi: 10.1186/s12889-021-10429-0.

R. Ocaña-riola, “The Use of Statistics in Health Sciences : Situation Analysis and Perspective,” no. October 2016, 2018, doi: 10.1007/s12561-015-9138-4.

L. Moyé, “Statistical methods for cardiovascular researchers,” Circ. Res., vol. 118, no. 3, pp. 439–453, 2016, doi: 10.1161/CIRCRESAHA.115.306305.

D. C. Yadav and S. Pal, “Prediction of heart disease using feature selection and random forest ensemble method,” Int. J. Pharm. Res., vol. 12, no. 4, pp. 56–66, 2020, doi: 10.31838/ijpr/2020.12.04.013.

D. Normawati and D. P. Ismi, “K-Fold Cross Validation for Selection of Cardiovascular Disease Diagnosis Features by Applying Rule-Based Datamining,” Signal Image Process. Lett., vol. 1, no. 2, pp. 23–35, 2019, doi: 10.31763/simple.v1i2.3.

C. Series, “Comparison of coronary heart disease stratification using the Jakarta cardiovascular score between main office and site office workers Comparison of coronary heart disease stratification using the Jakarta cardiovascular score between main office and site ,” 2018.

S. G. Kwak and S.-H. Park, “Normality Test in Clinical Research,” J. Rheum. Dis., vol. 26, no. 1, p. 5, 2019, doi: 10.4078/jrd.2019.26.1.5.

A. Ghasemi and S. Zahediasl, “Normality tests for statistical analysis: A guide for non-statisticians,” Int. J. Endocrinol. Metab., vol. 10, no. 2, pp. 486–489, 2012, doi: 10.5812/ijem.3505.

H. Taherdoost, “Validity and Reliability of the Research Instrument; How to Test the Validation of a Questionnaire/Survey in a Research,” SSRN Electron. J., no. September, 2018, doi: 10.2139/ssrn.3205040.