The Utilisiation of composite Machine Learning models for the Classification of Medical Datasets For Sickle Cell Disease

The increase growth of health information systems has provided a significant way to deliver great change in medical domains. Up to this date, the majority of medical centres and hospitals continue to use manual approaches for determining the correct medication dosage for sickle cell disease. Such methods depend completely on the experience of medical consultants to determine accurate medication dosages, which can be slow to analyse, time consuming and stressful. The aim of this paper is to provide a robust approach to various applications of machine learning in medical domain problems. The initial case study addressed in this paper considers the classification of medication dosage levels for the treatment of sickle cell disease. This study base on different architectures of machine learning in order to maximise accuracy and performance. The leading motivation for such automated dosage analysis is to enable healthcare organisations to provide accurate therapy recommendations based on previous data. The results obtained from a range of models during our experiments have shown that a composite model, comprising a Neural Network learner, trained using the Levenberg-Marquardt algorithm, combined with a Random Forest learner, produced the best results when compared to other models with an Area under the Curve of 0.995

A Framework to Support Ubiquitous Healthcare Monitoring and Diagnostic for Sickle Cell Disease

Recent technology advances based on smart devices have improved the medical facilities and become increasingly popular in association with real-time health monitoring and remote/personals health-care. Healthcare organisations are still required to pay more attention for some improvements in terms of cost-effectiveness and maintaining efficiency, and avoid patients to take admission at hospital. Sickle cell disease (SCD) is one of the most challenges chronic obtrusive disease that facing healthcare, affects a large numbers of people from early childhood. Currently, the vast majority of hospitals and healthcare sectors are using manual approach that depends completely on patient input, which can be slowly analysed, time consuming and stressful. This work proposes an alert system that could send instant information to the doctors once detects serious condition from the collected data of the patient. In addition, this work offers a system that can analyse datasets automatically in order to reduce error rate. A machine-learning algorithm was applied to perform the classification process. Two experiments were conducted to classify SCD patients from normal patients using machine learning algorithm in which 99 % classification accuracy was achieved using the Instance-based learning algorithm

Training Neural networks for Experimental models: Classifying Biomedical Datasets for Sickle Cell Disease

This paper presents the use of various type of neural network architectures for the classification of medical data. Extensive research has indicated that neural networks generate significant improvements when used for the pre-processing of medical time-series data signals and have assisted in obtaining high accuracy in the classification of medical data. Up to date, most of hospitals and healthcare sectors in the United Kingdom are using manual approach for analysing patient input for sickle cell disease, which depends on clinician’s experience that can lead to time consuming and stress to patents. The results obtained from a range of models during our experiments have shown that the proposed Back-propagation trained feed-forward neural network classifier generated significantly better outcomes over the other range of classifiers. Using the ROC curve, experiments results showed the following outcomes for our models, in order of best to worst: Back-propagation trained feed-forward neural net classifier: 0.989, Functional Link neural Network: 0.972, in comparison to the Radial basis neural Network Classifiers with areas of 0.875, and the Voted Perception classifier: 0.766. A Linear Neural Network was used as baseline classifier to illustrate the importance of the previous models, producing an area of 0.849, followed by a random guessing model with an area of 0.524

A Data Science Methodology Based on Machine Learning Algorithms for Flood Severity Prediction

In this paper, a novel application of machine learning algorithms including Neural Network architecture is presented for the prediction of flood severity. Floods are considered natural disasters that cause wide scale devastation to areas affected. The phenomenon of flooding is commonly caused by runoff from rivers and precipitation, specifically during periods of extremely high rainfall. Due to the concerns surrounding global warming and extreme ecological effects, flooding is considered a serious problem that has a negative impact on infrastructure and humankind. This paper attempts to address the issue of flood mitigation through the presentation of a new flood dataset, comprising 2000 annotated flood events, where the severity of the outcome is categorised according to 3 target classes, demonstrating the respective severities of floods. The paper also presents various types of machine learning algorithms for predicting flood severity and classifying outcomes into three classes, normal, abnormal, and high-risk floods. Extensive research indicates that artificial intelligence algorithms could produce enhancement when utilised for the pre-processing of flood data. These approaches helped in acquiring better accuracy in the classification techniques. Neural network architectures generally produce good outcomes in many applications, however, our experiments results illustrated that random forest classifier yields the optimal results in comparison with the benchmarked models

Machine Learning approaches to the application of Disease Modifying Therapy for Sickle Cell using Classification Models

This paper discusses the use of machine learning techniques for the classification of medical data, specifically for guiding disease modifying therapies for Sickle Cell. Extensive research has indicated that machine learning approaches generate significant improvements when used for the pre-processing of medical time-series data signals and have assisted in obtaining high accuracy in the classification of medical data. The aim of this paper is to present findings for several classes of learning algorithm for medically related problems. The initial case study addressed in this paper involves classifying the dosage of medication required for the treatment of patients with Sickle Cell Disease. We use different machine learning architectures in order to investigate the accuracy and performance within the case study. The main purpose of applying classification approach is to enable healthcare organisations to provide accurate amount of medication. The results obtained from a range of models during our experiments have shown that of the proposed models, recurrent networks produced inferior results in comparison to conventional feedforward neural networks and the Random Forest model. Results have also indicated that for the recurrent network models tested, the Jordan architecture was found to yield significantly better outcomes over the range of performance measures considered. For our dataset, it was found that the Random Forest Classifier produced the highest levels of performance overall

Evaluating student levelling based on machine learning model’s performance

In this paper, a novel application of machine learning algorithms is presented for student levelling. In multicultural countries such as UAE, there are various education curriculums where the sector of private schools and quality assurance is supervising various private schools for many nationalities. As there are various education curriculums in United Arab Emirates, specifically Abu Dhabi, to meet expats’ needs, there are different requirements for registration and success. In addition, there are different age groups for starting education in each curriculum. Every curriculum follows different education methods such as assessment techniques, reassessment rules, and exam boards. Currently, students who transfer to other curriculums are not correctly placed to their appropriate year group as a result of the start and end dates of each academic year as well as due to their date of birth, in which students who are either younger or older for that year group can create gaps in their learning and performance. In addition, pupils’ academic journeys are not stored which create a gap for the schools to track their learning process. In this paper, we propose a computational framework applicable in multicultural countries such as United Arab Emirates in which multi-education systems are implemented. Machine Learning are used to provide the appropriate student’ level aiding schools to provide a smooth transition when assigning students to their year groups and provide levelling and differentiation information of pupils for a smooth transition between one education curriculums to another, in which retrieval of their progress is possible. For classification and discriminant analysis of pupils levelling, three machine learning classifiers are utilised including random forest classifier, Artificial Neural Network, and combined classifiers. The simulation results indicated that the proposed machine learning classifiers generated effective performance in terms of accuracy

Study circles improve the precision in nutritional care in special accommodations

Background: Disease-related malnutrition is a major health problem in the elderly population, but it has until recently received very little attention, especially are management issues under-explored. By identifying residents at the risk of undernutrition, appropriate nutritional care can be provided. Objectives: Do study circles and policy documents improve the precision in nutritional care and decrease the prevalence of low or high BMI? Design: Pre and post intervention study. Setting: Special accommodations (nursing homes) within six municipalities were involved. Participants: In 2005, 1726 (90.4%) out of 1910 residents agreed to participate and in 2007, 1526 (81.8%) out of 1866 residents participated. Intervention: Study circles in one municipality, having a policy document in one municipality and no intervention in four municipalities. Measurements: Risk of undernutrition was defined as involving any of: involuntary weight loss, low BMI, and/or eating difficulties. Overweight was defined as high BMI. Results: In 2005 and 2007, 64% of 1726 and 66% of 1526 residents respectively were at the risk of undernutrition. In 2007 significantly more patients in the study circle municipality were accurately provided protein and energy enriched food compared to in the no intervention municipalities. There was a decrease in the prevalence of low BMI in the study circle municipality and the prevalence of overweight increased in the policy document municipality between 2005 and 2007

p16INK4A Positively Regulates Cyclin D1 and E2F1 through Negative Control of AUF1

/pRB/E2F pathway, a key regulator of the critical G1 to S phase transition of the cell cycle, is universally disrupted in human cancer. However, the precise function of the different members of this pathway and their functional interplay are still not well defined. -dependent manner, and several of these genes are also members of the AUF1 and E2F1 regulons. We also present evidence that E2F1 mediates p16-dependent regulation of several pro- and anti-apoptotic proteins, and the consequent induction of spontaneous as well as doxorubicin-induced apoptosis. is also a modulator of transcription and apoptosis through controlling the expression of two major transcription regulators, AUF1 and E2F1

Characterization of CTX-M ESBLs in Enterobacter cloacae, Escherichia coli and Klebsiella pneumoniae clinical isolates from Cairo, Egypt

<p>Abstract</p> <p>Background</p> <p>A high rate of resistance to 3^rdgeneration cephalosporins among Enterobacteriaceae isolates from Egypt has been previously reported. This study aims to characterize the resistance mechanism (s) to extended spectrum cephalosporins among resistant clinical isolates at a medical institute in Cairo, Egypt.</p> <p>Methods</p> <p>Nonconsecutive <it>Klebsiella pneumoniae </it>(Kp), <it>Enterobacter cloacae </it>(ENT) and <it>Escherichia coli </it>(EC) isolates were obtained from the clinical laboratory at the medical institute. Antibiotic susceptibility was tested by CLSI disk diffusion and ESBL confirmatory tests. MICs were determined using broth microdilution. Isoelectric focusing (IEF) was used to determine the pI values, inhibitor profiles, and cefotaxime (CTX) hydrolysis by the β-lactamases. PCR and sequencing were performed using <it>bla</it>_CTX-Mand IS<it>Ecp1</it>-specific primers, with DNA obtained from the clinical isolates. Conjugation experiments were done to determine the mobility of <it>bla</it>_CTX-M.</p> <p>Results</p> <p>All five clinical isolates were resistant to CTX, and were positive for ESBL screening. IEF revealed multiple β-lactamases produced by each isolate, including a β-lactamase with a pI of 8.0 in Kp and ENT and a β-lactamase with a pI of 9.0 in EC. Both β-lactamases were inhibited by clavulanic acid and hydrolyzed CTX. PCR and sequence analysis identified <it>bla</it>_CTX-M-14in Kp and ENT and a <it>bla</it>_CTX-M-15in EC. Both <it>bla</it>_CTX-M-14and <it>bla</it>_CTX-M-15were preceded by IS<it>Ecp1 </it>elements as revealed by partial sequence analysis of the upstream region of the <it>bla</it>_CTX-Mgenes. <it>bla</it>_CTX-M-15 was transferable but not <it>bla</it>_CTX-M-14.</p> <p>Conclusion</p> <p>This is the first report of CTX-M-14 in Kp and ENT isolates from Egypt, the Middle East and North Africa.</p