Artery/vein classification of retinal vessels using classifiers fusion

Abstract

The morphological changes in retinal blood vessels indicate cardiovascular diseases and consequently those diseases lead to ocular complications such as Hypertensive Retinopathy. One of the significant clinical findings related to this ocular abnormality is alteration of width of vessel. The classification of retinal vessels into arteries and veins in eye fundus images is a relevant task for the automatic assessment of vascular changes. This paper presents an important approach to solve this problem by means of feature ranking strategies and multiple classifiers decision-combination scheme that is specifically adapted for artery/vein classification. For this, three databases are used with a local dataset of 44 images and two publically available databases, INSPIRE-AVR containing 40 images and VICAVR containing 58 images. The local database also contains images with pathologically diseased structures. The performance of the proposed system is assessed by comparing the experimental results with the gold standard estimations as well as with the results of previous methodologies, achieving promising classification performance, with an over all accuracy of 90.45%, 93.90% and 87.82%, in retinal blood vessel separation for Local, INSPIRE-AVR and VICAVR dataset, respectively.

Document recommendation based on interests of co-authors for brain science

Abstract

Personalized knowledge recommendation is an effective measure to provide individual information services in the field of brain science. It is essential that a complete understanding of authors’ interests and accurate recommendation are carried out to achieve this goal. In this paper, a collaborative recommendation method based on co-authorship is proposed to make. In our approach, analysis of collaborators’ interests and the calculation of collaborative value are used for recommendations. Finally, the experiments using real documents associated with brain science are given and provide supports for collaborative document recommendation in the field of brain science.

An E-health system for monitoring elderly health based on Internet of Things and Fog computing

Abstract

With the significant increase in the number of elderly in the world and the resulting health problems of these increasing, finding technical solutions to address this problem has become a pressing necessity, particularly in the field of health care. This paper proposes an e-health system for monitoring elderly health based on the Internet of Things (IoT) and Fog computing. The system was developed using Mysignals HW V2 platform and an Android app that plays the role of Fog server, which enables the collection of physiological parameters and general health parameters from elderly periodically. This Android app enables also the elderly and their families to follow their health, and they can also communicate with health care providers (administrators and doctors) and receive recommendations, notifications and alerts. By evaluating this system, we find the most users they consider useful, easy to use and learn, suggesting that our proposal can improve the quality of health care for elderly.

Colon cancer data analysis by chameleon algorithm

Abstract

Detecting the key differential genes of colon cancers is very important to tell colon cancer patients from normal people. A gene selection algorithm for colon cancers is proposed by using the dynamic modeling properties of chameleon algorithm and its capability to discover any arbitrary shape clusters. This chameleon algorithm based gene selection algorithm comprises three steps. The first step is to select those genes with higher Fisher function values as candidate genes. The second step is to detect gene groups by using chameleon algorithm based on Euclidean distance. The third step is to select the most important gene from each gene cluster to comprise the gene subset by using the information index to classification of each gene. After that the chameleon algorithm is used to detect groups of colon cancer patients and normal people only with genes in gene subset. The final clustering accuracy of chameleon algorithm with the selected genes is up to 85.48%. The clustering analysis to colon cancer data and the comparisons to the other related studies demonstrate that the proposed algorithm is effective in detecting the differential genes of colon cancers.

Neural attention with character embeddings for hay fever detection from twitter

Abstract

The paper aims to leverage the highly unstructured user-generated content in the context of pollen allergy surveillance using neural networks with character embeddings and the attention mechanism. Currently, there is no accurate representation of hay fever prevalence, particularly in real-time scenarios. Social media serves as an alternative to extract knowledge about the condition, which is valuable for allergy sufferers, general practitioners, and policy makers. Despite tremendous potential offered, conventional natural language processing methods prove limited when exposed to the challenging nature of user-generated content. As a result, the detection of actual hay fever instances among the number of false positives, as well as the correct identification of non-technical expressions as pollen allergy symptoms poses a major problem. We propose a deep architecture enhanced with character embeddings and neural attention to improve the performance of hay fever-related content classification from Twitter data. Improvement in prediction is achieved due to the character-level semantics introduced, which effectively addresses the out-of-vocabulary problem in our dataset where the rate is approximately 9%. Overall, the study is a step forward towards improved real-time pollen allergy surveillance from social media with state-of-art technology.

Modeling and classification of voluntary and imagery movements for brain–computer interface from fNIR and EEG signals through convolutional neural network

Abstract

Practical brain–computer interface (BCI) demands the learning-based adaptive model that can handle diverse problems. To implement a BCI, usually functional near-infrared spectroscopy (fNIR) is used for measuring functional changes in brain oxygenation and electroencephalography (EEG) for evaluating the neuronal electric potential regarding the psychophysiological activity. Since the fNIR modality has an issue of temporal resolution, fNIR alone is not enough to achieve satisfactory classification accuracy as multiple neural stimuli are produced by voluntary and imagery movements. This leads us to make a combination of fNIR and EEG with a view to developing a BCI model for the classification of the brain signals of the voluntary and imagery movements. This work proposes a novel approach to prepare functional neuroimages from the fNIR and EEG using eight different movement-related stimuli. The neuroimages are used to train a convolutional neural network (CNN) to formulate a predictive model for classifying the combined fNIR–EEG data. The results reveal that the combined fNIR–EEG modality approach along with a CNN provides improved classification accuracy compared to a single modality and conventional classifiers. So, the outcomes of the proposed research work will be very helpful in the implementation of the finer BCI system.

Imputation techniques on missing values in breast cancer treatment and fertility data

Abstract

Clinical decision support using data mining techniques offers more intelligent way to reduce the decision error in the last few years. However, clinical datasets often suffer from high missingness, which adversely impacts the quality of modelling if handled improperly. Imputing missing values provides an opportunity to resolve the issue. Conventional imputation methods adopt simple statistical analysis, such as mean imputation or discarding missing cases, which have many limitations and thus degrade the performance of learning. This study examines a series of machine learning based imputation methods and suggests an efficient approach to in preparing a good quality breast cancer (BC) dataset, to find the relationship between BC treatment and chemotherapy-related amenorrhoea, where the performance is evaluated with the accuracy of the prediction. To this end, the reliability and robustness of six well-known imputation methods are evaluated. Our results show that imputation leads to a significant boost in the classification performance compared to the model prediction based on listwise deletion. Furthermore, the results reveal that most methods gain strong robustness and discriminant power even the dataset experiences high missing rate (> 50%).

Cognitive modelling of Chinese herbal medicine’s effect on breast cancer

Abstract

Purpose

Traditional Chinese medicine (TCM) has recently attracted increasing interests in cancer treatment. It was found that TCM-based treatment, combined with other therapies, can help improve patients’ life quality. However, the existing research in TCM lacks a systematic modelling for the causal relationship of the factors related to the diagnosis and decision making.

Methods

In this paper, we proposed the use of fuzzy cognitive map (FCM) to represent the cognition of TCMs usage in cancer treatment.

Results

Through a case analysis, we analyse and summarise the effects of Chinese herbal medicine in breast cancer management.

Conclusion

FCMs can visually represent the cognitive knowledge, particularly the causal relationship among key factors of TCM effects and the related breast cancer status.

Multi-objective semi-supervised clustering to identify health service patterns for injured patients

Abstract

Purpose

This study develops a pattern recognition method that identifies patterns based on their similarity and their association with the outcome of interest. The practical purpose of developing this pattern recognition method is to group patients, who are injured in transport accidents, in the early stages post-injury. This grouping is based on distinctive patterns in health service use within the first week post-injury. The groups also provide predictive information towards the total cost of medication process. As a result, the group of patients who have undesirable outcomes are identified as early as possible based health service use patterns.

Methods

We propose a multi-objective optimization model to group patients. An objective function is the cost function of k-medians clustering to recognize the similar patterns. Another objective function is the cross-validated root-mean-square error to examine the association with the total cost. The best grouping is obtained by minimizing both objective functions. As a result, the multi-objective optimization model is a semi-supervised clustering which learns health service use patterns in both unsupervised and supervised ways. We also introduce an evolutionary computation approach includes stochastic gradient descent and Pareto optimal solutions to find the optimal solution. In addition, we use the decision tree method to reproduce the optimal groups using an interpretable classification model.

Results

The results show that the proposed multi-objective semi-supervised clustering identifies distinct groups of health service uses and contributes to predict the total cost. The performance of the multi-objective model has been examined using two metrics such as the average silhouette width and the cross-validation error. The examination proves that the multi-objective model outperforms the single-objective ones. In addition, the interpretable classification model shows that imaging and therapeutic services are critical services in the first-week post-injury to group injured patients.

Conclusion

The proposed multi-objective semi-supervised clustering finds the optimal clusters that not only are well-separated from each other but can provide informative insights regarding the outcome of interest. It also overcomes two drawback of clustering methods such as being sensitive to the initial cluster centers and need for specifying the number of clusters.