Mohammed Khalaf

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. For our dataset, it was found that the Random Forest Classifier produced the highest levels of performance overall.

Training Neural Networks as Experimental Models: Classifying Biomedical Datasets for Sickle Cell Disease

This paper discusses 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 patients. 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 Receiver Operating Characteristic 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.

Robust Approach for Medical Data Classification and Deploying Self-Care Management System for Sickle Cell Disease

Intelligent systems and smart devices have played the major role in improving the healthcare organisation in terms of continuous tele-monitoring therapy and maintaining telemedicine management system for sickle cell disease. The biggest challenge facing majority of patients is the fact that there is still a lack of communication with healthcare professionals. Smart home (out-of hospital care) can raise personal self-sufficiency in association with living independently for longer as this disease is considered life-long condition. By using a self-care management system, we tend to improve patient welfare and mitigate patient illness before it gets worse over time, particularly with elderly people. This paper describes the state of the art in pervasive healthcare applications and the communication technologies that assist healthcare providers to offer better services for patients. This research proposes an alert system that could send immediate information to the medical consultants once detects serious condition from the collected data of the patient. Furthermore, the system is able to track various types of symptoms through mobile application in the purpose of obtaining support from medical specialists when it is required. A machine-learning algorithm was conducted to perform the classification process. Four experiments were carried out to classify sickle cell disease patients from normal patients using machine-learning algorithm in which 99.5984% classification accuracy was achieved using Multi-layer perceptron. Classification using Core Vector Regression, Hyper Pipes and Zero-Rule based algorithms achieved classification accuracy of 95.9839 %, 87.9518% and 70.6827 %, respectively.

Artificial Intelligence for Detecting Preterm Uterine Activity in Gynecology and Obstetric Care

Preterm birth brings considerable emotional and economic costs to families and society. However, despite extensive research into understanding the risk factors, the prediction of patient mechanisms and improvements to obstetrical practice, the UK National Health Service still annually spends more than £2.95 billion on this issue. Diagnosis of labour in normal pregnancies is important for minimizing unnecessary hospitalisations, interventions and expenses. Moreover, accurate identification of spontaneous preterm labour would also allow clinicians to start necessary treatments early in women with true labour and avert unnecessary treatment and hospitalisation for women who are simply having preterm contractions, but who are not in true labour. In this research, the Electrohysterography signals have been used to detect preterm births, because Electrohysterography signals provide a strong basis for objective prediction and diagnosis of preterm birth. This has been achieved using an open dataset, which contains 262 records for women who delivered at term and 38 who delivered prematurely. Three different machine learning algorithm were used to identify these records. The results illustrate that the Random Forest performed the best of sensitivity 97%, specificity of 85%, Area under the Receiver Operator curve (AUROC) of 94% and mean square error rate of 14%.

The utilisation 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.

A Dynamic Neural Network Architecture with Immunology Inspired Optimization for Weather Data Forecasting

Recurrent neural networks are dynamical systems that provide for memory capabilities to recall past behaviour, which is necessary in the prediction of time series. In this paper, a novel neural network architecture inspired by the immune algorithm is presented and used in the forecasting of naturally occurring signals, including weather big data signals. Big Data Analysis is a major research frontier, which attracts extensive attention from academia, industry and government, particularly in the context of handling issues related to complex dynamics due to changing weather conditions. Recently, extensive deployment of IoT, sensors, and ambient intelligence systems led to an exponential growth of data in the climate domain. In this study, we concentrate on the analysis of big weather data by using the Dynamic Self Organized Neural Network Inspired by the Immune Algorithm. The learning strategy of the network focuses on the local properties of the signal using a self-organised hidden layer inspired by the immune algorithm, while the recurrent links of the network aim at recalling previously observed signal patterns. The proposed network exhibits improved performance when compared to the feedforward multilayer neural network and state-of-the-art recurrent networks, e.g., the Elman and the Jordan networks. Three non-linear and non-stationary weather signals are used in our experiments. Firstly, the signals are transformed into stationary, followed by 5-steps ahead prediction. Improvements in the prediction results are observed with respect to the mean value of the error (RMS) and the signal to noise ratio (SNR), however to the expense of additional computational complexity, due to presence of recurrent links.

A Performance Evaluation of Systematic Analysis for Combining Multi-class Models for Sickle Cell Disorder Data Sets

Machine learning approach is considered as a field of science aiming specifically to extract knowledge from the data sets. The main aim of this study is to provide a sophisticate model to difference applications of machine learning models for medically related problems. We attempt for classifying the amount of medications for each patient with Sickle Cell disorder. We present a new technique to combine two classifiers between the Levenberg-Marquartdt training algorithm and the k-nearest neighbours algorithm. In this paper, we introduce multi-class label classification problem in order to obtain training and testing methods for each models along with other performance evaluations. In machine learning, the models utilise a training sets in association with building a classifier that provide a reliable classification. This research discusses different aspects of machine learning approaches for the classification of biomedical data. We are mainly focus on the multi-class label classification problem where many number of classes are available in the data sets. Results have indicated that for the machine learning models tested, the combination classifiers were found to yield considerably better results over the range of performance measures that been selected for this research.

Lumbar Spine Discs Labeling Using Axial View MRI Based on the Pixels Coordinate and Gray Level Features

Disc herniation is a major reason for lower back pain (LBP), a health issue that affects a very high proportion of the UK population and is costing the UK government over £1.3 million per day in health care cost. Currently, the process to diagnose the cause of LBP involves examining a large number of Magnetic Resonance Images (MRI) but this process is both expensive in terms time and effort. Automatic labeling of lumbar disc pixels in the MRI to detect the herniation area will reduce the time to diagnose and detect the cause of LBP by the physicians. In this paper, we present a method for automatic labeling of the lumbar spine disc pixels in axial view MRI using pixels locations and gray level as features. Clinical MRIs are used for the training and testing of the method. The pixel classification accuracy and the quality of the reconstructed disc images are used as the main performance indicators for our method. Our experiments show that high level of classification accuracy of 91.1% and 98.9% can be achieved using Weighted KNN and Fine Gaussian SVM classifiers respectively.

The utilisation of social media for bridging the gap between patients and pharmaceutical companies

This paper examines the current relationships between pharmaceutical companies and patients, to understand the barriers they face when trying to engage with one another and also assessing the feasibility of using social media to break these barriers, to overall increase patient support and trust. Extensive research is conducted through the literature review, where clear linkages are highlighted between; Social media and Pharma, Pharma and Patients and finally Patients and Social Media. The research indicated that pharmaceutical companies are threading in the water when it comes to utilising social media, due to lack of regulations on adverse events reporting. However, if pharmaceutical companies start to utilise social media for disease awareness campaigns and social support, it will provide patients with more support and build trust over time.