Showing 3 results for Decision Tree
A Saki Malehi, E Hajizadeh, R Fatemi,
Volume 8, Issue 2 (9-2012)
Abstract
Background
& Objectives: Identifying
the important influential factors is a great challenge in oncology studies.
Decision tree is one of methods that could be used to evaluate the prognostic
factors and classifying the patients' homogeneously. This method identifies the
main prognostic factors and then determines the subgroups of patients based on
those prognostic factors. The aim of this study was to assess the prognostic
factors and homogeneous subgroups of colorectal patient through survival tree.
Methods: Data collected from an observational
of 739 colorectal patients registered in the cancer registry affiliated to the
center of Research Center of Gastroenterology and Liver Disease (RCGLD), Shahid
Beheshti Medical University, Tehran, Iran. Death was the interested event and
the survival time was calculated from date of diagnosis until occurrence of
event (or censoring) in months. Finally we used decision tree based method for
classifying and analyzing the data.
Results: Based on our result, decision tree
identified four covariates as important prognostic factors in 0.05 significant
levels: general stage of cancer, age of diagnosis, histology of tumor and
morphology type of tumor. Also patients based on these prognostic factors
divided into five homogeneous subgroups. The greater values of measure of
separation (SEP) criterion support the appropriateness of this model for such
the data.
Conclusion:
Decision tree is powerful and intuitive method. It has a key feature that is in
addition to evaluate the prognostic factors, provides the homogeneous subgroups
for future analysis.
F Feizmanesh, Aa Safaei,
Volume 14, Issue 3 (12-2018)
Abstract
Background and Objectives: Pulmonary embolism is a potentially fatal and prevalent event that has led to a gradual increase in the number of hospitalizations in recent years. For this reason, it is one of the most challenging diseases for physicians. The main purpose of this paper was to report a research project to compare different data mining algorithms to select the most accurate model for predicting pulmonary embolism in hospitalized patients. This model would provide the knowledge needed by the medical staff fir better decision making.
Methods: In this research, we designed a prediction model using different methods of machine learning that would best predict the probability of pulmonary embolism in patients at risk. Among data mining algorithms, Bayesian network, decisions tree (J48), logistic regression (LR), and sequential minimal optimization (SMO) were used. The data used in the study included risk factors and past history of patients admitted to the Lung Department of Shariati Hospital, Tehran, Iran.
Results: The results showed that the accuracy and specificity of all prediction models were satisfactory. The Bayesian model had the highest sensitivity in predicting pulmonary embolism.
Conclusion: Although the results showed a little difference in the performance of prediction models, the Bayesian model is a more appropriate tool to predict the occurrence of pulmonary embolism in hospitalized patients in this type of data. It can be considered a supportive approach along medical decisions to improve disease prediction.
Mohammad Khajedaluee, Maliheh Dadgar Moghaddam, Amir-Reza Khajedaluee, Hiva Sharebiani, Hamidreza Bahrami Taghanaki, Maryam Ziadi Lotfabadi, Zeinab Shateri Amiri,
Volume 18, Issue 4 (3-2023)
Abstract
Background and Objectives: Cardiovascular diseases are the leading cause of adult mortality in many developing countries. This study aims to compare the estimation of the ten-year relative risk of cardiovascular events using the Framingham criteria with a native model.
Methods: This population-based cross-sectional study was conducted in 2014, focusing on the adult population (≥16 years) of Mashhad. Stratified random cluster sampling was employed to gather participants' information based on Framingham's criteria. Data mining, utilizing the decision tree algorithm design, was evaluated using Rapidminer v5.3 software and the cross-validation method.
Results: Out of 2978 individuals, 1930 (64.9%) were women and 1041 (35.1%) were men, with a mean age of 43.5±14.7. Applying the Framingham criteria, the ten-year risk levels of cardiovascular disease were estimated as follows: 77.8% at a low-risk level, 13.4% at a medium-risk level, and 8.8% at a high-risk level.
Regarding data mining, model number (1) achieved an accuracy of 79.56%, indicating that the predicted risk levels using the Framingham algorithm matched the observed values at 95.24% for the low-risk level, 90.8% for the medium-risk level, and 33.13% for the high-risk level. As for model number (2), an accuracy of 82.78% was obtained, with the matching values being 98.20% for the low-risk level, 0.42% for the medium-risk level, and 53.01% for the high-risk level.
Conclusion: The Framingham criteria demonstrate limited effectiveness in predicting medium and high-risk levels in the Mashhad population. According to the local model, smoking and high blood pressure in adulthood are the most significant factors in predicting the risk of cardiovascular diseases in young individuals.
