Improved Alzheimer’s disease detection by MRI using multimodal machine learning algorithms

Dementia is one of the huge medical problems that have challenged the public health sector around the world. Moreover, it generally occurred in older adults (age > 60). Shockingly, there are no legitimate drugs to fix this sickness, and once in a while it will directly influence individual memory abilities and diminish the human capacity to perform day by day exercises. Many health experts and computing scientists were performing research works on this issue for the most recent twenty years. All things considered, there is an immediate requirement for finding the relative characteristics that can figure out the identification of dementia. The motive behind the works presented in this thesis is to propose the sophisticated supervised machine learning model in the prediction and classification of AD in elder people. For that, we conducted different experiments on open access brain image information including demographic MRI data of 373 scan sessions of 150 patients. In the first two works, we applied single ML models called support vectors and pruned decision trees for the prediction of dementia on the same dataset. In the first experiment with SVM, we achieved 70% of the prediction accuracy of late-stage dementia. Classification of true dementia subjects (precision) is calculated as 75%. Similarly, in the second experiment with J48 pruned decision trees, the accuracy was improved to the value of 88.73%. Classification of true dementia cases with this model was comprehensively done and achieved 92.4% of precision. To enhance this work, rather than single modelling we employed multi-modelling approaches. In the comparative analysis of the machine learning study, we applied the feature reduction technique called principal component analysis. This approach identifies the high correlated features in the dataset that are closely associated with dementia type. By doing the simultaneous application of three models such as KNN, LR, and SVM, it has been possible to identify an ideal model for the classification of dementia subjects. When compared with support vectors, KNN and LR models comprehensively classified AD subjects with 97.6% and 98.3% of accuracy respectively. These values are relatively higher than the previous experiments. However, because of the AD severity in older adults, it should be mandatory to not leave true AD positives. For the classification of true AD subjects among total subjects, we enhanced the model accuracy by introducing three independent experiments. In this work, we incorporated two new models called Naïve Bayes and Artificial Neural Networks along support vectors and KNN. In the first experiment, models were independently developed with manual feature selection. The experimental outcome suggested that KNN 3 is the optimal model solution because of 91.32% of classification accuracy. In the second experiment, the same models were tested with limited features (with high correlation). SVM was produced a high 96.12% of classification accuracy and NB produced a 98.21% classification rate of true AD subjects. Ultimately, in the third experiment, we mixed these four models and created a new model called hybrid type modelling. Hybrid model performance is validated AU-ROC curve value which is 0.991 (i.e., 99.1% of classification accuracy) has achieved. All these experimental results suggested that the ensemble modelling approach with wrapping is an optimal solution in the classification of AD subjects

Designing of an Expert system for the management of Seafarer's health

In general merchant ships do not have medical facilities on board. When seafarer got sickness or accident, either ship captain or officers who are in charge will assist them, but these people do not have enough medical knowledge. To overcome this, we developed a Seafarer Health Expert System (SHES) that can facilitate telemedical services in an emergency. A comprehensive analysis of seafarers' medical issues that were conducted from medical records of patients assisted on board ships by the International Radio Medical Center (C.I.R.M.), Italy. Data mining techniques are involved to manage epidemiological data analysis in a two-phase setup. In the first phase, the common pathologies that occurred onboard were analyzed, later a detailed questionnaire for each medical problem was developed to provide precise symptomatic information to the onshore doctor. In this paper, we mainly highlighted the SHES framework, design flow, and functionality. Besides, nine designing policies and three actors with separate working panels were clearly described. The proposed system is easy and simple to operate for anyone of no computer experience and create medical requests for the fast delivery of symptomatic information to an onshore doctor

Second wave of COVID-19 in Italy: Preliminary estimation of reproduction number and cumulative case projections

The second wave of a novel coronavirus in Italy has caused 247,369 new cases and 1782 deaths only in October 2020. This significantly alarming infectious disease controlling board to impose again mitigation measures for controlling the epidemic growth. In this paper, we estimate the latest COVID-19 reproduction number (R_0) and project the epidemic size for the future 45 days. The R_0 value has calculated as 2.83 (95% CI: 1.5-4.2) and the cumulative incidences 100,015 (95% CI; 73,201-100,352), and daily incidences might be reached up to 15,012 (95% CI: 8234-16,197) respectively

Generative adversarial network: An overview of theory and applications

Abstract In recent times, image segmentation has been involving everywhere including disease diagnosis to autonomous vehicle driving. In computer vision, this image segmentation is one of the vital works and it is relatively complicated than other vision undertakings as it needs low-level spatial data. Especially, Deep Learning has impacted the field of segmentation incredibly and gave us today different successful models. The deep learning associated Generated Adversarial Networks (GAN) has presenting remarkable outcomes on image segmentation. In this study, the authors have presented a systematic review analysis on recent publications of GAN models and their applications. Three libraries such as Embase (Scopus), WoS, and PubMed have been considered for searching the relevant papers available in this area. Search outcomes have identified 2084 documents, after two-phase screening 52 potential records are included for final review. The following applications of GAN have been emerged: 3D object generation, medicine, pandemics, image processing, face detection, texture transfer, and traffic controlling. Before 2016, research in this field was limited and thereafter its practical usage came into existence worldwide. The present study also envisions the challenges associated with GAN and paves the path for future research in this realm

Machine learning in medicine: Performance calculation of dementia prediction by support vector machines (SVM)

Machine Learning (ML) is considered as one of the contemporary approaches in predicting, identifying, and making decisions without having human involvement. ML is quickly evolving in the medical industry ranging from diagnosis to visualization of diseases and the study of disease transmission. These algorithms were developed to identify the problems in medical image processing. Numerous studies previously attempted to apply these algorithms on MRI (Magnetic Resonance Image) data to predict AD (Alzheimer's disease) in advance. The present study aims to explore the usage of support vector machine (SVM) in the prediction of dementia and validate its performance through statistical analysis. Data is obtained from the Open Access Series of Imaging Studies (OASIS-2) longitudinal collection of 150 subjects of 373 MRI data. Results provide evidence that better performance values for dementia prediction are achieved by low gamma (1.0E-4) and high regularized (C = 100) values. The proposed approach is shown to achieve accuracy and precision of 68.75% and 64.18%. Keywords: Machine learning, OASIS, Support vector machines, Kernel, Gamma, Regularization (C

Performance analysis of different machine learning algorithms in breast cancer predictions

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Are telemedicine systems effective healthcare solutions during the COVID-19 pandemic?

On 9 January 2020, China’s Centres for Disease Control and Prevention (CDC) reported that a novel coronavirus causing a severe acute respiratory syndrome (SARS-CoV-2) had been identified as the causative agent of an aggressive respiratory disease, later referred to as coronavirus disease 2019 (COVID19).1 As of 18 January 2021, there have been over 90 million reported cases of COVID-19 and the virus has been responsible for nearly 2.5 million deaths.2 The COVID-19 emergency has required continued contingency plans, making it necessary to both rethink the current approach to healthcare as well as how to adapt to the emerging needs of healthcare in the context of a pandemic. We have learned how to mitigate the spread of the virus by implementing social distancing measures, enforcing proper mask compliance, and reducing face-to-face contact in a health setting unless absolutely necessary. Community spread from the virus must be prevented to minimise the risks of infection for health professionals. In this respect, essential telemedicine services may help safeguard public health in significant ways.3 T

Applications of metaverse for improving healthcare at sea

The provision of adequate healthcare on board ships has always represented a challenge for medicine. In general, ships are at sea for days or weeks before they can reach a port and with the only exception of some large passenger or cruise ships, they do not carry health professionals on board. The maritime healthcare sector has expanded more quickly as a result of the quickening pace of digitalisation and automation, which has led to the creation of new models and new opportunities for seafarers’ treatment provision at reduced costs. By enabling both onboard patients and medical personnel to have lifelike experiences, a new digital technology known as the metaverse has relevant potential for the healthcare of seafarers

COVID-19 outbreak reproduction number estimations and forecasting in Marche, Italy

Background: COVID-19 disease is becoming a global pandemic and more than 200 countries were affected because of this disease. Italy is one of the countries is largely suffered with this virus outbreak, and about 180,000 cases (as of 20 April 2020) were registered which explains the large transmissibility and reproduction case numbers. Objective: In this study, we considered the Marche region of Italy to compute different daily transmission rates (Rt) including five provinces in it. We also present forecasting of daily and cumulative incidences associated after the next thirty days. The Marche region is the 8th in terms of number of people infected in Italy and the first in terms of diffusion of the infection among the 4 regions of the center of Italy. Methods: Epidemic statistics were extracted from the national Italian Health Ministry website. We considered outbreak information where the first case registered in Marche with onset symptoms (26 February 2020) to the present date (20 April 2020). Adoption of incidence and projections with R statistics was done. Results: The median values of Rt for the five provinces of Pesaro and Urbano, Ancona, Fermo, Ascoli Piceno, and Macerata, was 2.492 (1.1-4.5), 2.162 (1.0-4.0), 1.512 (0.75-2.75), 1.141 (1.0-1.6), and 1.792 (1.0-3.5) with 95% of CI achieved. The projections at end of 30th day of the cumulative incidences 323 (95% CI), and daily incidences 45 (95% CI) could be possible. Conclusions: This study highlights the knowledge of essential insights into the Marche region in particular to virus transmission dynamics, geographical characteristics of positive incidences, and the necessity of implementing mitigation procedures to fight against the COVID-19 outbreak