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Showing 3 results for Risk Factors
Relationship Between Number Of Involved Coronary Artery With Some Risk Factors By Angiography 
Seyed Abedein Hosseini, Ali Akbar Abdollahi, Naser Behnampour, Aref Salehi,
Volume 6, Issue 5 (1-2013)
Abstract

Background and Aim: Despite the information regarding CAD risk factors, there isn't agreement between the relation of this risk factors and coronary artery diseases. This study was done for determination of related factors with vessels involved in coronary artery angiography.

Materials and Methods: In this descriptive and analytical study, 2390 patients' .were selected via census sampling from Kosar Angiography center in the Golestan province. Data gathering form included data such as age, gender, body mass index (BMI), blood pressure, diabetes, smoking and opiates addiction history. Vessels involved were determined by angiography. Data analysis was done with one way ANOVAs and logistic regression using SPSS 16 soft ware.

Results: Mean and standard deviation of patient's age was 57.9±10. 58.2 percent of them were male. There were significant correlations between age, gender and BMI with numbers of vessels involved. Male gender(OR=1.329), hypertension (OR=1.25) and diabetes(OR=1.20) increased the probability of more than one vessels involvement. Regression analysis showed there were no significant correlations between age, BMI, smoking and opiates addiction history with more than one vessels involvement.

Conclusion: Our finding confirmed that male gender, hypertension and diabetes are the main risk factors in involvement of more than one vessel.


Decision Modeling for Diagnosis and Screening of Ovarian Epithelial Cancer
Reza Safdari, Leila Shahmoradi, Marjan Daneshvar, Elmira Pourtorkan, Mersa Gholamzadeh,
Volume 12, Issue 1 (5-2018)
Abstract
Background and Aim: The Ovarian epithelial cancer is one of the most deadly types of cancers in women.Thus, the purpose of this study was to investigate the most effective factors in predicting and detecting Ovarian cancer in the form of a decision tree to facilitate the Ovarian cancer diagnosis.
Materials and Methods: The present study was a descriptive-developmental study. The main research tool applied in this study was a checklist which was designed based on the medical records, published studies, scientific references, and expert consultation.To determine the content validity of the checklist, the CVR method was applied. Next, survey research was done with aid of Likert-based checklist based on expert opinions in gynecology. Finally, to develop the decision tree, the results of the expert survey were analyzed and the final model was implemented based on the survey results.
Results: The data elements of final decision tree were derived from the result of expert surveys, guidelines, clinical pathways and strategies in context of diagnosis and screening of Ovarian cancer. The leaf nodes in the tree include different types of tumor markers, following up, therapeutic measures, and referrals. The accuracy of the decision tree was approved by the experts. The most important tumor markers that obtained from the decision model in this study were CA19-9, ROMA (CA125 + HE4) and CEA.
Conclusion: Clinical decision models can provide specific diagnosis and therapeutic suggestions by creating patient information integration framework. The model developed in this study can improve the diagnosis of epithelial Ovarian cancer considerably by facilitating decision making.

Diagnosis of Breast Cancer Using Decision Tree, Artificial Neural Network and Naive Bayes to Provide a Native Model for Fars Province
Azita Yazdani, Ali Asghar Safaei, Reza Safdari, Maryam Zahmatkeshan,
Volume 13, Issue 3 (9-2019)
Abstract
Background and Aim: Breast cancer is the most common type of cancer and the main cause of death from cancer in women worldwide. Technologies such as data mining, have enabled experts in this area to improve decision making in the early diagnosis of the disease. Therefore, the purpose of this research is to develop an automatic diagnostic model for breast cancer by employing data mining methods and selecting the model with the highest accuracy of diagnosis.
Materials and Methods: In this study, 654 available patient records of Motahari breast cancer Clinic in Shiraz" were used as the sample. The number of records was reduced to 621 after the pre-processing operation. These samples had 22 features that ultimately used ten were used as effective features in the design of the model. Three types of Decision tree, Naive Bayes and Artificial neural network were used for diagnosis of breast cancer and 10-fold cross-validation method for constructing and evaluating the model on the collected data set.
Results: The results of the three techniques mentioned all three models showed promising results in detecting breast cancer. Finally, the artificial neural network accounted for the highest accuracy of 94/49%(sensitivity 96/19%, specificity 86/36%) in the diagnosis of breast cancer.
Conclusion:  Based on the results of the decision tree, the risk factors such as age, weight, Age of menstruation, menopause, OCP of records duration, and the age of the first pregnancy were among the factors affecting the incidence of breast cancer in women. 

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