Informatica

The main purpose of this article was to compare traditional binary logistic regression analysis with decision tree analysis for the evaluation of the risk of cardiovascular diseases in adult men living in the city. Patients and methods. In our study, we used data from the Multifactorial Ischemic Heart Disease Prevention Study (MIHDPS). In the MIHDPS study, a random sample of male inhabitants of Kaunas city (Lithuania) aged 40–59 years was examined between 1977 and 1980. We analysed a sample of 5626 men. Taking blood pressure lowering medicine, disability, intermittent claudication, regular smoking, a higher value of the body mass index, systolic blood pressure, age, total serum cholesterol, and walking in winter were associated with a higher probability of ischemic heart disease or cardiovascular diseases. Having more siblings and drinking alcohol were associated with a lower probability of these diseases. The binary logistic regression method showed a very slightly lower level of errors than the decision tree did (the difference between the two methods was 2.04% for ischemic heart disease (IHD) and 2.86% for cardiovascular disease (CVD), but for consumers, the decision tree is easier to understand and interpret the results. Both of these methods are appropriate to analyse cardiovascular disease data.

The paper deals with the analysis of the two survival models of the accelerated failure‐time using two‐parametrical log‐logistic and Weibull distributions, and survival models using conditional generalized Weibull, log‐logistic, and Smith and Bain distributions. The observed survival (number of deaths during the 30‐year follow‐up period among the study cohort) and the survival predicted by regression models (predicted number of deaths for the same period of time) were compared. Data on deaths occurring in random sample of men were obtained from the death register of the city of Kaunas. The best agreement between the predicted and observed survival was obtained with one of the modified Smith and Bain models.