Exploring the Hidden Challenges Associated with the Evaluation of Multi-class Datasets using Multiple Classifiers

The optimization and evaluation of a pattern recognition system requires different problems like multi-class and imbalanced datasets be addressed. This paper presents the classification of multi-class datasets which present more challenges when compare to binary class datasets in machine learning. Furthermore, it argues that the performance evaluation of a classification model for multi-class imbalanced datasets in terms of simple “accuracy rate” can possibly provide misleading results. Other parameters such as failure avoidance, true identification of positive and negative instances of a class and class discrimination are also very important. We, in this paper, hypothesize that “misclassification of true positive patterns should not necessarily be categorized as false negative while evaluating a classifier for multi-class datasets”, a common practice that has been observed in the existing literature. In order to address these hidden challenges for the generalization of a particular classifier, several evaluation metrics are compared for a multi-class dataset with four classes; three of them belong to different neurodegenerative diseases and one to control subjects. Three classifiers, linear discriminant, quadratic discriminant and Parzen are selected to demonstrate the results with examples

Early Detection of Neurodegenerative Diseases from Bio-Signals: A Machine Learning Approach

Given the fact that people, especially in advanced countries, are living longer due to the advancements in medical sciences which resulted in the prevalence of age-related diseases like Alzheimer’s and dementia. The occurrence of such diseases continues to increase and ultimately the cost of caring for these groups will become unsustainable. Addressing this issue has reached a critical point and failing to provide a strategic way forward will negatively affect patients, national health services and society as a whole.Three distinctive development stages of neurodegenerative diseases (Retrogenesis, Cognitive Impairment and Gait Impairment) motivated me to divide this research work into two main parts. To fully achieve the purpose of early detection/diagnosis, I aimed at analysing the gait signals as well as EEG signals, separately, as both of these signals severely get affected by any neurological disease.The first part of this research work focuses on the discrimination analysis of gait signals of different neurodegenerative diseases (Parkinson’s, Huntington, and Amyotrophic Lateral Sclerosis) and also of control subjects. This involves relevant feature extraction, solving the issues of imbalanced datasets and missing entries and lastly classification of multiclass datasets. For the classification and discrimination of gait signals, eleven (11) classifiers are selected representing linear, non-linear and Bayes normal classification techniques. Results revealed that three classifiers have provided us with higher accuracy rate which are UDC, LDC and PARZEN with 65%, 62.5% and 60% accuracy, respectively. Further, I proposed and developed a new classifier fusion strategy that combined classification algorithms with combining rules (voting, product, mean, median, maximum and minimum). It generates better results and classifies subjects more accurately than base-level classifiers.The last part of this research work is based on the rectification and computation of EEG signals of mild Alzheimer’s disease patients and control subjects. To detect the perturbation in EEG signals of Alzheimer’s patients, three neural synchrony measurement techniques; phase synchrony, magnitude squared coherence and cross correlation are applied on three different databases of mild Alzheimer’s disease (MiAD) patients and healthy subjects. I have compared right and left temporal parts of brain with rest of the brain area (frontal, central and occipital), as temporal regions are relatively the first ones to be affected by Alzheimer’s. Two novel methods are proposed to compute the neural synchronization of the brain; Average synchrony measure and PCA based synchrony measure. These techniques are evaluated for three different datasets of MiAD patients and control subjects using the Wilcoxon ranksum test (Mann-Whitney U test). Results demonstrated that PCA based method helped us to find more significant features that can be used as biomarkers for the early diagnosis of Alzheimer’s

Machine Learning Approaches and Web-Based System to the Application of Disease Modifying Therapy for Sickle Cell

Sickle cell disease (SCD) is a common serious genetic disease, which has a severe impact due to red blood cell (RBCs) abnormality. According to the World Health Organisation, 7 million newborn babies each year suffer either from the congenital anomaly or from an inherited disease, primarily from thalassemia and sickle cell disease. In the case of SCD, recent research has shown the beneficial effects of a drug called hydroxyurea/hydroxycarbamide in modifying the disease phenotype. The clinical management of this disease-modifying therapy is difficult and time consuming for clinical staff. This includes finding an optimal classifier that can help to solve the issues with missing values, multi-class datasets, and features selection. For the classification and discriminant analysis of SCD datasets, 7 classifiers based on machine learning models are selected representing linear and non-linear methods. After running these classifiers with a single model, the results revealed that a single classifier has provided us with effective outcomes in terms of the classification performance evaluation metric. In order to produce such an optimal outcome, this research proposed and designed combined classifiers (ensemble classifiers) among the neural network’s models, the random forest classifier, and the K-nearest neighbour classifier. In this aspect, combining the levenberg-marquardt algorithm, the voted perceptron classifier, the radial basis neural classifier, and random forest classifier obtain the highest rate of performance and accuracy. This ensemble classifier receives better results during the training set and testing set process. 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/personal health-care. The web-based system developed under the supervision of the haematology specialist at the Alder Hey Children’s Hospital in order to produce such an effective and useful system for both patients and clinicians. To sum up, the simulation experiment concludes that using machine learning and the web-based system platforms represents an alternative procedure that could assist healthcare professionals, particularly for the specialist nurse and junior doctor to improve the quality of care with sickle cell disorder

An automated classification system to determine malignant grades of brain tumour (glioma) in magnetic resonance images based on meta-trainable multiple classifier schemes

The accurate classification of malignant grades of brain tumours is crucial for therapeutic planning as it impacts on the tumour’s prognosis, where the higher the malignancy levels of the brain tumour are, the higher the mortality rate is. It is also essential to provide patients with appropriate clinical management that may prolong survival and improve their quality of life. Determining the malignant grade of a brain tumour is a critical challenge because different malignant grades of brain tumours, in some cases, have inconsistent and mixed morphological characteristics. Consequently, the visual diagnosis using only the naked eye is a very complex and challenging task. The most common type of brain tumour is glioma. According to the World Health Organisation, low-grade glioma, which includes grade I and grade II are the least malignant, slow growing, and respond well to treatment. While, high-grade gliomas, which include grade III and grade IV are extremely malignant, have a poor prognosis and may lead to a high mortality rate. Hence, the motivation to develop an automated classification system to predict the malignant grade of glioma is the aim of this research. To achieve this aim, several novel methods were developed and this includes new methods for the extraction of statistical measures, selection of the dominant predictors, and the fusion of multi-classification models. The integration of these stages generates an accurate and automated decision system to determine the malignant grade of glioma. The feature extraction starts from the viewpoint that the objective measure of the brain tumour descriptors in MR images lead to an accurate classification of malignant brain tumours. This work starts from the standpoint that meta-trainable fusion of multiple classifier models can offer a better classification accuracy to recognise the malignant grade of glioma in MR images. This study developed a novel strategy based on two stages of multiple classifier systems for glioma grades. In the first stage, different machine learning algorithms were used. In the second stage, a systematic trainable combiner was designed based on deep neural networks. This research was validated using four benchmark datasets of MR images, which are publicly available and confirmed with the histopathological diagnosis. The proposed system was also evaluated and compared against different traditional algorithms; the experimental results showed that the proposed system has successfully achieved better and optimal discrimination in glioma grades on all dataset