QUE NUESTROS OBJETIVOS PARA EL ALUMNADO SE CUMPLEN.

This study proposes the utilisation of artificial intelligence systems to enhance the environment of the medical domain offered to patients who suffer from chronic SCD. In order to improve the quality of care for patient and clinicians, this research focused on two important perspectives. Firstly, this study used machine-learning algorithms based on real blood test datasets for those who suffer from SCD. The main purpose of doing this is to improve the classification process for this chronic disease. Secondly, this study designed a user-friendly platform based on a web- based management system to build strong communication and follow-up between patients and healthcare providers.

This kind of study is proposed by the Alder Hey Children’s Hospital to improve the quality of life, reduce time for the NHS, and obtain accurate results depending on the patient’s blood test. In point of fact, implementing machine learning for the classification process could help healthcare providers through reducing the need of medical expert’s assessment as they able to learn from data that been diagnosed previously. This type of approach is able to assist specialist nurses and junior doctor to improve their decision-making process.

This research was inspired by the urgent need for a new pathway that could reduce the burden on the shoulders of NHS, and at the same time enhance the quality of patients’ lives. In fact, the use of machine-learning methods as a diagnostic model could reduce the need for specialist assessment as they can learn from previously diagnosed patients to diagnose new cases. These machine-learning based on diagnostic models used to train non-specialist doctors to improve their decision-making procedure.

Extensive research indicates that artificial intelligence such as the machine learning models produce a good improvement with clinical datasets and have helped in acquiring high accuracy. The main aim of this study is to provide a sophisticated model to differentiate applications of machine learning approaches for medically related problems. This study attempts to classify the amount of medication for each patient with Sickle Cell disorder. This research uses different architectures in terms of examining performance for each model within this study. The motivation for the classification approach used in this study is to support medical sectors to

162 | P a g e offer proper therapy advice depending on the former dataset. Expert systems and various Artificial Intelligence methods and techniques have been used and developed to improve decision support tools for medical purposes. Machine Learning models (ML) is considered to be a powerful technique in the field of scientific research that enables computers to learn from data [13]. There are a number of machine learning techniques for classification include the Artificial Neural Network, the Random Forest model, and the Support Vector Machine. In this paper, the application of machine learning approaches for the problem of SCD medication dose management is considered.

As mentioned in chapter 2, patients with SCD have long-term conditions and they can tackle their critical conditions using the proposed SCD web-based system. This research proposed a management and follow-up platform; with prototype implementations to illustrate in the real- world domain. Our solution system addressed the issues with chapter 2 as there is no sufficient system to deal with SCD at present. It resolved the issue of direct communication between the SCD patients and healthcare providers. This study met several patients and parents at the Alder Hey Children’s Hospital and investigated the acceptance of using such a web-based system. The system was also handed to the healthcare providers to follow-up with patient’s requirements. Patients and clinicians were happy to work with the web-based system platform and to use it with the medical domain.

8.2

Research Contributions

The significance and the research contribution can be assessed from two aspects; the machine learning and web-based system in association with medical domain and IT prospective. This experiment not only deal with causes and symptoms of SCD but has concentrated on an important field where artificial intelligent system and IT can play a key role to provide proper treatment for SCD patients. Moreover, it has discovered further innovations in the domain of machine learning models, pre-processing medical datasets, classification task, and performance evaluation techniques metrics. In addition, to expand the life expectancy and diagnose the life- threatening symptoms for sickle cell disorder patients, it is exploring some crucial hidden features that can be employed as biomarkers.

The real datasets were collected from a local NHS trust foundation trust hospital over a 6-year period. After obtaining full ethical approval to implement our system at the hospital site and collecting more datasets, this research managed to receive 1896 samples from the haematology department. This study noticed some samples had minority classes, which led us to use a

163 | P a g e statistical technique to avoid this issue. In order to find a suitable solution for Skewed Datasets, this research have elaborated in more detail in chapter 5 and chapter 6 the importance of solving datasets with minority class to avoid inaccurate or biased datasets. One of the possible solutions is to use over-sampling that used in our empirical study about increasing the number of samples. In order to find the best classifiers that can yield best accuracy and performance, this study selected a number of models as shown in chapter 4. These classifiers divided into linear and non-linear. Initially, this research used only single classifiers to estimate the classification performance evaluation metrics with 6 significant categories. Then, this study used ensemble classifiers to improve the results that obtained from single models. The results show that assembling models with high sensitivity, specificity, F-1measures, J1-score, accuracy and AUC values can provide optimal classification with high rate as illustrated in the result and simulation analysis chapter. In this aspect, combining LEVNN, VPC, RBNC, RFC based on the LEVNN obtain the highest rate of performance and accuracy. This ensemble classifier received better results during training set process including; sensitivity 0.99111, specificity 0.98367, Precision 0.89367, F1 0.93933, J1 0.97478, Accuracy 0.98467, AUC 0.99833. Where the neural network and Random forest received better results during the testing set process including; sensitivity 0.87778, specificity 0.90856, Precision 0.55922, F1 0.67389, J1 0.78622, Accuracy 0.90644, AUC 0.93789. The outcomes of this experiment encouraged us to use different kinds of artificial intelligence techniques to provide more accurate results. This study used visualization methods and statistical techniques to present our results. This has assisted us to make comparison on the outcomes from different aspects and finally to choose the best classifiers that can be proper to our SCD datasets and can be implemented within the clinical domain.

Medical experts need to investigate through patient’s outcomes, which include numerical data and data plots to support patient with their medication. To handle this matter, designed an additional Clinicians Web-based management system, ideally to support doctors. In order to achieve this issue, designed a robust web-based system for patients and clinicians. Our main target was to offer a user-friendly web-based system capable of making on-demand, decision support system and recommendations that could lead to good improvements. This research discovered that the potential of such web-based system is effective and useful tools for healthcare providers to recommend therapy. Because of this procedure, the clinician’s platform system sends instant information to the patients based on their blood test samples. Then, patients can review their blood test results in electronic version, which can lead to improvement

164 | P a g e in their health condition. Moreover, this study promoted linear graphs to show patients if there is any significant improvement made in the past months in terms of haemoglobin or foetal haemoglobin. These two blood characteristics are considered important for healthcare professionals to check patients’ condition with SCD. The selected SCD user is expected to receive instant email and can view outcomes. Eventually, based on the doctors’ experience, this research designed a dynamic page for junior doctors and specialist nurses that can help them to provide the accurate amount of medication based on the patient blood test results. In our interview with SCD patients, all the patients have signed the concert form and promised to use the system in the near future.