Introduction: The Adaptive neuro-fuzzy inference system (ANFIS) is a soft computing model based on neural network precision and fuzzy decision-making advantages, which can highly facilitate diagnostic modeling. In this study, this model was used for breast cancer detection. Methods: A set of 1508 records of cancerous and non-cancerous participants' risk factors was employed for this study. First, the risk factors were classified into three priorities according to their importance level, were then fuzzified and the subtractive clustering method was used for their input with the same order. Randomly, the dataset was divided into two groups of 70% and 30% of the total records, and used for training and testing the new model, respectively. After the training, the system was separately tested with the Wisconsin and real clinical data, and the results were reported. Results: The desired fuzzy functions were defined for the variables, and the model was trained with the combined dataset. Testing was conducted first with 30% of that dataset, and then with the real data obtained from a real clinical (BCRC) data, while the model's precision for the above stages was 81% (sensitivity = 85.1%, specificity = 74.5%) and 84.5% (sensitivity = 89.3%, specificity = 79.9%) respectively. Conclusions: A final ANFIS model was developed and tested for two standard and real datasets on breast cancer. The resulting model could be employed with high precision for the BCRC Clinic's database, as well as conducting similar studies and re-evaluating other databases.
Context:As breast cancer treatment going forward, need for supportive strategies grows. That creates an important call to summarize what has been done regionally.Objectives: In this study, we systematically reviewed articles that proceeded rehabilitation and supportive care in breast cancer patients in Iran to present a research map of rehabilitation research in the past 10 years in Iran.Data Sources: All articles published from January 2006 to October 2015 were included. All of the breast cancer studies in Iran were searched in 3 English (Web of Science, PubMed, and Scopus) and 2 Persian databases (SID and IranMedex).Study Selection: All papers related to rehabilitation in breast cancer were included and categorized into 5 subgroups including qualitative, instrument, lymphedema, interventional, and observational studies. Three reviewers (two surgeons and an epidemiologist) screened the primary search and divided it into subgroups.Data Extraction: Two reviewers used a checklist to critically appraise the full text of the selected articles. The necessary information of retrieved articles was extracted and recorded in the designed data extraction spreadsheet in Excel software.Results: A total of 194 articles (102 in English and 92 in Persian) were assessed for eligibility of inclusion in the review, of which, 121 were excluded, and 73 studies were kept. The included studies consisted of 14 on qualitative design, 5 studies in the translation and validation of research instruments, 7 articles in the field of lymphedema, 20 articles about different intervention modalities on breast cancer patients (including education, social status, psychological, exercise, etc.), and 27 observational studies about anxiety, depression, quality of life, sexual function, emotional distress, complementary medicine, lifestyle, etc.Conclusions: Most of the reviewed studies insisted on a prevalence of physical, psychological, functional, and spiritual problems of breast cancer survivors and their caregivers. Designing a mega project to offer a palliative and rehabilitation service package according to the needs of Iranian patients may become a priority in their health care system.
ObjectiveTo define a core dataset for intensive care unit (ICU) patients outcome prediction in Iran. This core data set will lead us to design ICU outcome prediction models with the most effective parameters.MethodsA combination of literature review, national survey and expert consensus meetings were used. First, a literature review was performed by a general search in PubMed to find the most appropriate models for intensive care mortality prediction and their parameters. Second, in a national survey, experts from a couple of medical centres in all parts of Iran were asked to comment on a list of items retrieved from the earlier literature review study. In the next step, a multi-disciplinary committee of experts was installed. In four meetings, each data item was examined separately and included/excluded by committee consensus.ResultsThe combination of the literature review findings and experts’ consensus resulted in a draft dataset including 26 data items. Ninety-two percent of data items in the draft dataset were retrieved from the literature study and the others were suggested by the experts. The final dataset of 24 data items covers patient history and physical examination, chemistry, vital signs, oxygenations and some more specific parameters.ConclusionsThis dataset was designed to develop a nationwide prognostic model for predicting ICU mortality and length of stay. This dataset opens the door for creating standardised approaches in data collection in the Iranian intensive care unit estimation of resource utility.
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