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Showing 5 results for Risk Factor
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.


Determination Of Breast Density Using Fuzzy Logic
Mostafa Langarizadeh, Rozi Mahmud,
Volume 8, Issue 3 (9-2014)
Abstract

 Background and Aim: The risk of breast cancer increases directly in line with breast density. Therefore, it is important to pay more attention to denser breasts in order to detect abnormalities. The aim of this paper was to design and suggest a quantitative method to categorize breast density in digital mammogram images using fuzzy logic.

 Materials and Methods: This was a crosectional study which resulted in developing a new system. The research population was including patients who undergo mammography in National Cancer Society of Malaysia during 2010 to 2011. Sample included 220 mammogram images which was selected randomly. Data analysis was done using SPSS with Kappa statistics.

 Results: Accuracy level of 92.8% was obtained based on evaluation of the system and there was a strong correlation between the system output and radiologists’ estimation (K=0.87, p=0.0001).

 Conclusion : Results obtained from the suggested system had higher performance than similar systems. Therefore, it could be concluded that the fuzzy logic may be used in this area. In addition, such systems could be helpful for physicians.


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. 

Decision Model Making for Colorectal Cancer Screening Based on Risk Determination
Raoof Nopour, Mohammad Shirkhoda, Sharareh Rostam Niakan Kalhori,
Volume 14, Issue 2 (5-2020)
Abstract
Background and Aim: Colorectal cancer is one of the most common gastrointestinal cancers among human beings and the most important cause of death in the world. Based on the risk of colorectal cancer for individuals, using an appropriate screening program can help to prevent the disease. Therefore, the purpose of this study was to design a model for screening colorectal cancer based on risk factors to increase the survival rate of the disease on the one hand and to reduce the mortality rate on the other.
Materials and Methods: By reviewing articles and patients' records, 38 risk factors were detected. To determine the most important risk factors clinically, CVR(content validity ratio) was used; and considering the collected data, Spearman correlation coefficient and logistic regression analysis were applied for statistical analyses. Then, four algorithms -- J-48, J-RIP, PART and REP-Tree -- were used for data mining and rule generation. Finally, the most common model was obtained based on comparing the performance of the algorithms.
Results: After comparing the performance of algorithms, the J-48 algorithm with an F-Measure of 0.889 was found to be better than the others.
Conclusion: The results of evaluating J-48 data mining algorithm performance showed that this algorithm could be considered as the most appropriate model for colorectal cancer risk prediction.

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