جستجوی مقالات مرتبط با کلیدواژه "naive bayes" در نشریات گروه "پزشکی"

Cardiovascular diseases, including ischemic heart disease (IHD), are one of the main cause of mortality and morbidity worldwide and are currently one of the top ten causes of death. Ischemic heart disease is a type of heart disease that is caused by narrowing of arteries feeding the heart itself. The present study aimed to use data mining algorithms in screening and early prediction of IHD according to the patient's characteristics and risk factors.

In this research, data of the first phase of Yazd Health Study (YaHS), focusing on 21 characteristics of 10,000 participants aged 20-70 years such as age, type of chest pain, blood sugar level, body mass index, employment status, etc. which have been collected since 2013 were analyzed.

Data analysis was conducted using Random Forest and Naive Bayes algorithms which showed 74.51% accuracy in predicting IHD.

The study findings revealed that via applying Random Forest and Naive Bayes algorithms, ischemic heart disease can be predicted with high accuracy. Moreover, early screening and timely treatment in the early stages of disease may reduce mortality and morbidity.

 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.

 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.

 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.

  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.

Endometriosis is a prevalent disease in women which may lead to infertility or low fertility. Grasping the genetic grounds for the disease may contribute to its treatment because it is presumed that genetic factors predispose to endometriosis risk factors.
9 genes involved in endometriosis in patients suffering from endometriosis and also in healthy individuals (total 260 samples) were examined. The data were obtained from Ibn Sina (Jahad Daneshgahi) Research Center of new Technologies in Biological Sciences Institute. The study incorporated standard process Crisp for data mining. Weka data mining and software modeling were implemented with the aid of four algorithms.
Comparison of four algorithms implied prominent accuracy of K-Star model. Meanwhile, filtering, while reducing the percentage of models, presented a positive impact on the MLP model. The lowest percentage pertained to Naïve Bayes.
K-Star model without any filtering proved to have the highest accuracy in the early diagnosis of endometriosis.

Prediction of MicroRNAs (miRNAs) targets has a major importance. Development of calculating methods and also cost-effective and time-saving laboratory researches, has great effect on the production of therapeutic medicines like anti cancerous drugs. Since miRNAs have been recently identified, the researches trend was slow at first but by development of biologic databases and understanding its importance, scientists enhanced the speed of studies and paid more attention to this field. Until now, several computerized methods have been developed for prediction of microRNAs (miRNAs) targets but most of these methods have high false positive and further studies are required to improve these methods. Since, recent studies show that miRNAs have different targets in several tissues, this study aimed to provide a computerized method for prediction of miRNAs target in breast cancer.
In this study, at first, all types of features were extracted, then, dominant features were selected via CFS and Relief methods. Smart models such as nervous network, Support Vector Machine with three different cores, Naïve Bayes algorithm and Random Forest decision tree using ten cross accreditation method were tested and its results compared and analyzed. In order to validate the results, gene expression profiling was used.
Analyzing miRNA and gene expression profiles, the classifier predicted 124 functional interactions involving 21 miRNAs and 38 mRNAs in breast cancer.
In terms of bioinformatics, this approach was validated for breast cancer but for further validation, experimental methods also should be used.