Activities of daily living and lesion position among multiple sclerosis patients by Bayes network

Magnetic resonance imaging is a highly sensitive approach for diagnosis of multiple sclerosis, and T2-weighted images can reveal lesions in the cerebral white matter, gray matter, and spinal cord. However, the lesions have a poor correlation with measurable clinical disability. In this study, we performed a large-scale epidemiological survey of 238 patients with multiple sclerosis in eleven districts by network member hospitals in Shanghai, China within 1 year. The involved patients were scanned for position and size of lesions by MRI. Results showed that lesions in the cerebrum, spina cord, or supratentorial position had an impact on the activities of daily living in multiple sclerosis patients, as assessed by the Bayes network. On the other hand, brainstem lesions were very unlikely to influence the activities of daily living, and were not associated with the position of lesion, patient＇s gender, and patient＇s living place.

Adaptive Barebones Salp Swarm Algorithm with Quasi-oppositional Learning for Medical Diagnosis Systems: A Comprehensive Analysis

The Salp Swarm Algorithm(SSA)may have trouble in dropping into stagnation as a kind of swarm intelligence method.This paper developed an adaptive barebones salp swarm algorithm with quasi-oppositional-based learning to compensate for the above weakness called QBSSA.In the proposed QBSSA,an adaptive barebones strategy can help to reach both accurate convergence speed and high solution quality;quasi-oppositional-based learning can make the population away from traping into local optimal and expand the search space.To estimate the performance of the presented method,a series of tests are performed.Firstly,CEC 2017 benchmark test suit is used to test the ability to solve the high dimensional and multimodal problems;then,based on QBSSA,an improved Kernel Extreme Learning Machine(KELM)model,named QBSSA–KELM,is built to handle medical disease diagnosis problems.All the test results and discussions state clearly that the QBSSA is superior to and very competitive to all the compared algorithms on both convergence speed and solutions accuracy.

Generalized Oppositional Moth Flame Optimization with Crossover Strategy:An Approach for Medical Diagnosis

In the original Moth-Flame Optimization(MFO),the search behavior of the moth depends on the corresponding flame and the interaction between the moth and its corresponding flame,so it will get stuck in the local optimum easily when facing the multi-dimensional and high-dimensional optimization problems.Therefore,in this work,a generalized oppositional MFO with crossover strategy,named GCMFO,is presented to overcome the mentioned defects.In the proposed GCMFO,GOBL is employed to increase the population diversity and expand the search range in the initialization and iteration jump phase based on the jump rate;crisscross search(CC)is adopted to promote the exploitation and/or exploration ability of MFO.The proposed algorithm’s performance is estimated by organizing a series of experiments;firstly,the CEC2017 benchmark set is adopted to evaluate the performance of GCMFO in tackling high-dimensional and multimodal problems.Secondly,GCMFO is applied to handle multilevel thresholding image segmentation problems.At last,GCMFO is integrated into kernel extreme learning machine classifier to deal with three medical diagnosis cases,including the appendicitis diagnosis,overweight statuses diagnosis,and thyroid cancer diagnosis.Experimental results and discussions show that the proposed approach outperforms the original MFO and other state-of-the-art algorithms on both convergence speed and accuracy.It also indicates that the presented GCMFO has a promising potential for application.