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Cardiovascular disease is one of the most dangerous diseases that lead to death. It results from the lack of early detection of heart patients. Many researchers analyzed the risk factors of cardiovascular disease and proposed machine learning models for the early detection of heart patients. However, these models suffer from the high dimensionality of data and need to be improved to obtain highly accurate results. In this paper, a practical proposal is presented that can predict whether a patient has cardiovascular disease or not. The proposal was tested using five different standard data sets from the UCI repository. Our proposal consists of two main processes: the first is the data preprocessing process, and the second is the prediction process. In data preprocessing, the data is prepared for the prediction process, and three different feature selection methods (e.g., PCA) are applied to select the most relevant features from the data. In the prediction process, fourteen different prediction techniques (for example, Random Forest (RF) and Support Vector Classifier (SVC)) were applied to over-employed datasets. The techniques used were evaluated using four evaluation metrics: accuracy, precision, recall, and F1-score. The experimental results show that the LASSO method as a feature selection method with RF as a prediction technique produced the best accuracy (100%). Accuracy (99.57%) was obtained for Decision Tree (DT), Gradient Boosting (GB), AdaBoost (AB), Decision Tree Bagging Method (DTBM), Random Forest Bagging Method (RFBM), K-Nearest Neighbors Bagging Method (KNNBM), AdaBoost Boosting Method (ABBM), and Gradient Boosting Boosting Method (GBBM). The accuracy of SVC, Logistic Regression (LR), Naïve Bayes (NB), and Support Vector Classifier Bagging Method (SVCBM) was very similar to each other (98.73%).

Aim of the work:The aim of this study was to evaluate the effect of maternal anemia on fetal Doppler indices ; namely, umbilical artery and middle cerebral artery in the last trimester of pregnancy. Patients and Methods: This study was designed as a prospective case control clinical trial carried out in obstetric outpatient clinics and inpatient ward, Ain Shams University Maternity Hospital on 200 patients. The patients must follow these criteria:Gestational age between 28-40 weeks of singleton pregnancy (calculated by their last menstrual period or by earlier ultrasound), fetus is alive and normal fetal ultrasound parameters. Results: The umbilical artery resistance index showed a significant increase in moderate severe anemic patients more than the control group. Umbilical artery pulsatility index showed a significant increase in severe anemic group more than the other 3 groups.Umbilical artery systolic/diastolic ratio showed a significant increase in severe anemic more than the other groups. Finally, the umbilical artery cerebral/umbilical artery resistance ratio showed a significant increase in severe anemic more than the other groups. Conclusion: GA at delivery in different groups was matched, i.e. there was no statistical significant difference between different studied groups regarding GA at delivery (P > 0.05). Neonatal ICU admission were 2(4%), 2(4%), 4(8%) and 9(18%) in different groups, respectively. There was statistical significant difference between different studied groups regarding Neonatal ICU admission (P < 0.05).

Opinion mining in social networks data considers one of the most significant and challenging tasks in our days due to the huge number of information distributed each day. We can profit from these opinions by utilizing two significant procedures (classification and prediction). Although there is many researchers' interest in and work at this point, it still needs improvement. Therefore, in this paper, we present a method to improve the accuracy of the classification and prediction processes. The improvement is done through cleaning the data set by converting all words to lower case, removing usernames, mentions, links, repeated characters, numbers, delete more than two spaces between words, empty tweets, punctuations and stop words, and converting all words like "isn't" to "is not". In the feature selection phase, we use both unigrams and bigrams in order to extract the features from the data to training it. Our data set contains the user's feelings about distributed products, tweets labeled positive or negative, and each product rate from one to five. We implemented this work using different supervised machine learning algorithms like Naïve Bayes, Support Vector Machine and Max Entropy for the classification process, and Random Forest Regression, Logistic Regression, and Support Vector Regression for the prediction process. At last, we have accuracy in the classification and prediction process better than existing works. In classification, we achieved accuracy of 90% and in the prediction process, Support Vector Regression model is able to predict future product rate with a mean squared error (MSE) of 0.4122, Logistic Regression model is able to predict with a mean squared error of 0.4986 and Random Forest Regression model is able to predict with a mean squared error of 0.4770.