ANN-Based Prediction of Kidney Dysfunction Using Clinical Laboratory Data

This paper presents the prediction of Kidney dysfunction using probabilistic neural network (PNN). Six hundred and sixty (660) sets of analytical laboratory test have been collected from one of the private Clinical laboratories in Baghdad. For each subject, Serum urea and Serum creatinin levels have been analyzed and tested by using clinical laboratory measurements. The collected Urea and cretinine levels are then used as inputs to the Artificial Neural network model in which the training process is done by PNN which is a class of radial basis function (RBF) network is used as a classifier to predict whether Kidney is normal or it will have a dysfunction. The accuracy of Prediction, sensitivity and Specificity were found to be equal to 99%, 98% and 99% respectively for this proposed network .We conclude that the proposed model gives faster and more accurate prediction of Kidney dysfunction and it works as promising tool for predicting of routine kidney dysfunction from the clinical laboratory data

Detection of some antioxidant markers in saliva of patients with beta thalassemia major

Background : Although evaluation and maintenance of antioxidant defence can be useful in protecting β-thalassemia patients from more serious complications of the disease, there are limited studies about assessment of antioxidant capacity in beta-thalassemic patients particularly in saliva.Methodology:Thirty patients with β thalassemia major were involved in this study in thalassemia center / Ebn- Albalady hospital in Baghdad, and fifteen normal subjects with matched age and sex were also involved and considered as control group. Age, gender, blood groups, BMI, secretory status, and antioxidant markers were determined in the saliva of normal and diseased subjects, however the status of HCV infection, liver and spleen are evaluated in beta-thalassemic patients

Review of medical diagnostics via data mining techniques

Data mining is one of the most popular analysis methods in medical research. It involves finding patterns and correlations in previously unknown datasets. Data mining encompasses various areas of biomedical research, including data collection, clinical decision support, illness or safety monitoring, public health and inquiry research. Health analytics frequently uses computational methods for data mining, such as clustering, classification, and regression. Studies of large numbers of diverse heterogeneous documents, including biological and electronic information, provided medical and health studies.</jats:p

Breast Cancer Decisive Parameters for Iraqi Women via Data Mining Techniques

Objective This research investigates Breast Cancer real data for Iraqi women, these data are acquired manually from several Iraqi Hospitals of early detection for Breast Cancer. Data mining techniques are used to discover the hidden knowledge, unexpected patterns, and new rules from the dataset, which implies a large number of attributes.&#x0D; Methods Data mining techniques manipulate the redundant or simply irrelevant attributes to discover interesting patterns. However, the dataset is processed via Weka (The Waikato Environment for Knowledge Analysis) platform. The OneR technique is used as a machine learning classifier to evaluate the attribute worthy according to the class value.&#x0D; Results The evaluation is performed using a training data rather than cross validation. The decision tree algorithm J48 is applied to detect and generate the pattern of attributes, which have the real effect on the class value. Furthermore, the experiments are performed with three machine learning algorithms J48 decision tree, simple logistic, and multilayer perceptron using 10-folds cross validation as a test option, and the percentage of correctly classified instances as a measure to determine the best one from them. As well as, this investigation used the iteration control to check the accuracy gained from the three mentioned above algorithms. Hence, it explores whether the error ratio is decreasing after several iterations of algorithm execution or not.&#x0D; Conclusion It is noticed that the error ratio of classified instances are decreasing after 5-10 iterations, exactly in the case of multilayer perceptron algorithm rather than simple logistic, and decision tree algorithms. This study realized that the TPS_pre is the most common effective attribute among three main classes of examined dataset. This attribute highly indicates the BC inflammation.</jats:p

Comparison of the Effectiveness of Various Classifiers for Breast Cancer Detection Using Data Mining Methods

Countless women and men worldwide have lost their lives to breast cancer (BC). Although researchers from around the world have proposed various diagnostic methods for detecting this disease, there is still room for improvement in the accuracy and efficiency with which they can be used. A novel approach has been proposed for the early detection of BC by applying data mining techniques to the levels of prolactin (P), testosterone (T), cortisol (C), and human chorionic gonadotropin (HCG) in the blood and saliva of 20 women with histologically confirmed BC, 20 benign subjects, and 20 age-matched control women. In the proposed method, blood and saliva were used to categorize the severity of the BC into normal, benign, and malignant cases. Ten statistical features were collected to identify the severity of the BC using three different classification schemes&mdash;a decision tree (DT), a support vector machine (SVM), and k-nearest neighbors (KNN) were evaluated. Moreover, dimensionality reduction techniques using factor analysis (FA) and t-stochastic neighbor embedding (t-SNE) have been computed to obtain the best hyperparameters. The model has been validated using the k-fold cross-validation method in the proposed approach. Metrics for gauging a model&rsquo;s effectiveness were applied. Dimensionality reduction approaches for salivary biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 66.67% to 93.3% and 90%, respectively, by utilizing t-SNE and FA. Furthermore, dimensionality reduction strategies for blood biomarkers enhanced the results, particularly with the DT, thereby increasing the classification accuracy from 60% to 80% and 93.3%, respectively, by utilizing FA and t-SNE. These findings point to t-SNE as a potentially useful feature selection for aiding in the identification of patients with BC, as it consistently improves the discrimination of benign, malignant, and control healthy subjects, thereby promising to aid in the improvement of breast tumour early detection

Histological impact of nutritional style alteration in mice

Objectives: It is well established that diet and lifestyle are important in maintenance of healthy. Transition from a plant-based diet mostly to a high-calorie diet of animal products might raise the chronic diseases which called “degenerative”. This work aimed to study the histopathological effect of transition from complete plant-based diet to 10% animal products (sheep’s brain) on various body organs of mice.&#x0D; Methods: Eight-week old Balb/c male mice were divided into 2 groups (n=8); the first is restricted group in which mice were fed on restricted diet containing 10% of sheep’s brain homogenate, while the second is the control group in which fed on ad libitum on the diet for 7 days. During the duration of experiment, body weight and the amount of food intake were recorded daily, then at the end of experiment, all mice were sacrificed and various organs were obtained and processed for histopathological study.&#x0D; Results: the results showed that food intake by each mouse of restricted group are significantly lower than in control group. Although the mean of body weight in both groups revealed non-significant difference, the relative weight of various organs showed significant differences. On the other hand, sever histological changes were detected in all studied organs sections of restricted group.&#x0D; Conclusion: It can be concluded that changing in nutritional style rather than conventional diet play a crucial role in modifying the architectural aspects of different organs at tissue level. Therefore, these findings need further investigation at cellular, physiological, and molecular levels.</jats:p