Tayeb Mohammadi; Hadi Raeisi Shahraki; Jalal Poorolajal; Roya Najafi-Vosough; Khadijeh Najafi-Ghobadi; Javad Faradmal
Abstract
Background: Nowadays, breast cancer (BC) metastasis is a nightmare for women and one of the main challenges among researchers worldwide. Unlike traditional statistical methods that are not able to handle and take into account the complexity of effects and existence of interactions among predictor variables, ...
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Background: Nowadays, breast cancer (BC) metastasis is a nightmare for women and one of the main challenges among researchers worldwide. Unlike traditional statistical methods that are not able to handle and take into account the complexity of effects and existence of interactions among predictor variables, the decision trees can overcome these problems. This study aimed to predict and identify the main prognostic factors of BC metastasis status (binary response) using decision tree modeling.Methods: This retrospective cohort study was conducted on 375 patients with BC who had registered with the Comprehensive Cancer Control Center from 1998 to 2013. Some demographic features related to the conditions of the Person’s disease and the type of treatment received were recorded. We applied a tree-based approach using the Gini index as the homogeneity criterion to explore the factors affecting metastasis occurrence in BC patients.Results: The mean (SD) age of BC patients with and without metastasis was 55.7 (12.4) and 43.1 (7.2) years, respectively (P<0.001). The rate of metastasis was 33.3%. The five most important risk factors for metastasis of tumor proposed by tree diagram were age at diagnosis, grade of tumor, type of surgery, number of deliveries, and axillary surgery. The prediction accuracy of the proposed model was 84.3%, and its sensitivity and specificity were 66.4% and 93.2%, respectively.Conclusion: Age at diagnosis was the most important factor for predicting breast cancer metastasis, so that breast cancer patients aged over 54 were at high risk of metastasis.
Alireza Mirahmadizadeh; Sadaf Sahraian; Hamed Delam; Mozhgan Seif
Abstract
Objective: Type 2 diabetes is the most prevalent chronic disease in the world. Timely and appropriate control can significantly reduce the burdens and costs of this disease. Although insulin injection is the most efficient method to control type 2 diabetes, patients avoid this method for unknown reasons. ...
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Objective: Type 2 diabetes is the most prevalent chronic disease in the world. Timely and appropriate control can significantly reduce the burdens and costs of this disease. Although insulin injection is the most efficient method to control type 2 diabetes, patients avoid this method for unknown reasons. The main aim of the present study is to determine the factors influential in non-adherence to insulin using tools and models that have not been applied in this field so far.
Methods: The tendency to insulin injection in 457 patients with type 2 diabetes was investigated in this cross-sectional study using the classic logistic regression and new learning algorithms, including conditional tree, conditional forest, and random forest. Different fits were compared so that the best model can be determined to identify the factors in non-adherence to insulin.
Results: Although random forest had the highest accuracy among the fitted models, all the methods had a relative consensus that having life insurance, academic education, and insulin injection experience in immediate family members increase the tendency to accept insulin therapy. Our results also showed that younger patients and those who were committed to a specific diet better approved insulin therapy.
Conclusions: The reasons for non-adherence to insulin can be summarized in economic and psychological aspects. Therefore, the health system policies are recommended to address economic issues and also raise public awareness about this treatment method.
