Showing 5 results for Data Mining
S Setareh, M Zahiri Esfahani , M Zare Bandamiri , A Raeesi, R Abbasi,
Volume 14, Issue 1 (6-2018)
Abstract
Background and Objectives: Colon cancer is the third most common cancer in the world and the fourth most common cancer in Iran. It is very important to predict the cancer outcome and its basic clinical data. Due to to the high rate of colon cancer and the benefits of data mining to predict survival, the aim of this study was to survey two widely used machine learning algorithms, Bagging and Support Vector Machines (SVM), to predict the outcome of colon cancer patients.
Methods: The population of this study was 567 patients with stage 1-4 of colon cancer in Namazi Radiotherapy Center, Shiraz in 2006-2011. Three hundred and thirty eight patients were alive and 229 patients were dead. We used the Support Vector Machines (SVM) and Bagging methods in order to predict the survival of patients with colon cancer. The Weka software ver 3.6.10 was used for data analysis.
Results: The performance of two algorithms was determined using the confusion matrix. The accuracy, specificity, and sensitivity of the SVM was 84.48%, 81%, and 87%, and the accuracy, specificity, and sensitivity of Bagging was 83.95%, 78%, and 88%, respectively.
Conclusion: The results showed both algorithms have a high performance in survival prediction of patients with colon cancer but the Support Vector Machines has a higher accuracy.
F Ebrahimzadeh, E Hajizadeh, M Birjandi, S Feli, Sh Ghazi,
Volume 14, Issue 3 (12-2018)
Abstract
Background and Objectives: Academic failure is of paramount importance for medical students because it might lead to a decline in scientific level of the community of physicians in the future. This study was conducted to investigate the predictors of academic failure in medical students of Lorestan University of Medical Sciences using classification tree.
Methods: In this cohort study, academic records of all medical students of Lorestan University of Medical Sciences during the academic years of 1999-2008 were selected by census and were followed up until September 2016. Academic failure was defined as having at least one of the components of appropriate grade point average, prolonged graduation, academic probation, dropout, expulsion, and any failure in ccomprehensive exams and the CART classification tree was adopted using the SPSS 22 software to predict it.
Results: The cumulative incidence of academic failure was 26.4% and the most prevalent components were prolonged graduation (21.7%) and academic probation (15.0%). The probability of academic failure was 0.449 in subjects taking guest courses, 0.220 in subjects with no history of guest courses admitted to courses with less than 40 students and admission quotas of zone 1 or 3, and 0.456 in subjects with no history of guest courses admitted to courses with more than 40 students and males.
Conclusion: With respect to identifying the predictors of academic failure, it is suggested that these students be referred to consulting centers of the university or educational supervisors’ moreover, the regulations of taking guest courses in other universities should be revised.
Nasrin Talkhi, Nooshin Akbari Sharak, Zahra Rajabzadeh, Maryam Salari, Seyed Masoud Sadati, Mohammad Taghi Shakeri,
Volume 18, Issue 3 (12-2022)
Abstract
Background and Objectives: Due to the high prevalence of COVID-19 disease and its high mortality rate, it is necessary to identify the symptoms, demographic information and underlying diseases that effectively predict COVID-19 death. Therefore, in this study, we aimed to predict the mortality behavior due to COVID-19 in Khorasan Razavi province.
Methods: This study collected data from 51, 460 patients admitted to the hospitals of Khorasan Razavi province from 25 March 2017 to 12 September 2014. Logistic regression and Neural network methods, including machine learning methods, were used to identify survivors and non-survivors caused by COVID-19.
Results: Decreased consciousness, cough, PO2 level less than 93%, age, cancer, chronic kidney diseases, fever, headache, smoking status, and chronic blood diseases are the most important predictors of death. The accuracy of the artificial neural network model was 89.90% in the test phase. Also, the sensitivity, specificity and area under the rock curve in this model are equal to 76.14%, 91.99% and 77.65%, respectively.
Conclusion: Our findings highlight the importance of some demographic information, underlying diseases, and clinical signs in predicting survivors and non-survivors of COVID-19. Also, the neural network model provided high accuracy in prediction. However, medical research in this field will lead to complementary results by using other methods of machine learning and their high power.
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.
Asal Aghadavodian Jolfaee, Maryam Jahanbakhsh, Mohamad Sattari, Roya Kelishadi,
Volume 19, Issue 3 (12-2023)
Abstract
Background and Objectives: The present research was conducted to predict mental health based on three factors: nutrition, activity, and leisure time, among students in the adolescent age group, using data mining techniques.
Methods: The present analytical study was conducted on 14274 data available in the Caspian 5 database. According to the CRISP-DM method, data mining was done in 6 steps using decision trees, k nearest neighbors, simple Bayesian and random forest techniques in Rapidminer software.
Results: Among the four data mining techniques used to predict the mental health of adolescents based on nutrition, physical activity and leisure time, the random forest technique has the highest accuracy (91.72) and specificity (82.73) and the k-nearest neighbors technique has the highest sensitivity (96.30). In addition, based on random forest techniques, the rule with the highest level of support showed that an adolescent who is in high school, eats breakfast, lunch, and dinner every day, drinks tea and coffee weekly, exercises 2 hours a week at school,also, he has 4 days of physical activity for 30 minutes in the last week, and he goes to school with the service, with 100% confidence has good mental health.
Conclusion: Based on the random forest technique, which has showen the best performance, nutrition has the greatest impact on the mental health of Iranian adolescents. So, it is necessary to think about providing a suitable platform for training parents and adolescents regarding proper nutrition and increasing awareness in the field of adolescent mental health.
