An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening

About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results.This work was partially funded by Grant RTI2018-094283-B-C32, ECLIPSE-UA (Spanish Ministry of Education and Science)

Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: A Review

Autism spectrum disorder (ASD) research has yet to leverage big data on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data

Common human diseases prediction using machine learning based on survey data

In this era, the moment has arrived to move away from disease as the primary emphasis of medical treatment. Although impressive, the multiple techniques that have been developed to detect the diseases. In this time, there are some types of diseases COVID-19, normal flue, migraine, lung disease, heart disease, kidney disease, diabetics, stomach disease, gastric, bone disease, autism are the very common diseases. In this analysis, we analyze disease symptoms and have done disease predictions based on their symptoms. We studied a range of symptoms and took a survey from people in order to complete the task. Several classification algorithms have been employed to train the model. Furthermore, performance evaluation matrices are used to measure the model's performance. Finally, we discovered that the part classifier surpasses the others.Comment: 11 pages, 6 figures, accepted in Bulletin of Electrical Engineering and Informatics Journa

Early intervention for obsessive compulsive disorder : An expert consensus statement

© 2019 Elsevier B.V.and ECNP. All rights reserved.Obsessive-compulsive disorder (OCD) is common, emerges early in life and tends to run a chronic, impairing course. Despite the availability of effective treatments, the duration of untreated illness (DUI) is high (up to around 10 years in adults) and is associated with considerable suffering for the individual and their families. This consensus statement represents the views of an international group of expert clinicians, including child and adult psychiatrists, psychologists and neuroscientists, working both in high and low and middle income countries, as well as those with the experience of living with OCD. The statement draws together evidence from epidemiological, clinical, health economic and brain imaging studies documenting the negative impact associated with treatment delay on clinical outcomes, and supporting the importance of early clinical intervention. It draws parallels between OCD and other disorders for which early intervention is recognized as beneficial, such as psychotic disorders and impulsive-compulsive disorders associated with problematic usage of the Internet, for which early intervention may prevent the development of later addictive disorders. It also generates new heuristics for exploring the brain-based mechanisms moderating the ‘toxic’ effect of an extended DUI in OCD. The statement concludes that there is a global unmet need for early intervention services for OC related disorders to reduce the unnecessary suffering and costly disability associated with under-treatment. New clinical staging models for OCD that may be used to facilitate primary, secondary and tertiary prevention within this context are proposed.Peer reviewe

Strukturiranje domenskega znanja s pomočjo polavtomatske gradnje ontologij

In this paper, we present a case in semi-automatic ontology construction from literature. For this, we concentrate on the articles about autism obtained from the PubMed Central database. Our motivation was to investigate how separate parts of articles, such as titles, abstracts and full texts, influence the constructed ontology. Our results confirm the intuitive expectation that constructing ontologies from abstracts is a rational choice when uncovering the structure of a given scientific field. In addition, when compared to general knowledge of autism, ontology concepts from abstracts show the highest similarityV članku opisujemo primer polavtomatske gradnje ontologij iz literature. Članke, ki smo jih uporabili v opisanem primeru, smo pridobili iz baze Pubmed Central. Cilj naše raziskave je bil ugotoviti, kako uporaba posameznih delov člankov – naslovi, povzetki, cela besedila – vplivajo na zgrajeno ontologijo. Dobljeni rezultati potrjujejo intuitivno domnevo, da gradnja ontologije iz povzetkov daje najboljše rezultate pri odkrivanju zakonitosti na izbranem problemskem področju. Koncepti, ki smo jih evidentirali pri gradnji ontologij iz povzetkov člankov s področja avtizma, se najbolj ujemajo s splošnim znanjem o avtizmu

Gene selection and classification in autism gene expression data

Autism spectrum disorders (ASD) are neurodevelopmental disorders that are currently diagnosed on the basis of abnormal stereotyped behaviour as well as observable deficits in communication and social functioning. Although a variety of candidate genes have been attributed to the disorder, no single gene is applicable to more than 1–2% of the general ASD population. Despite extensive efforts, definitive genes that contribute to autism susceptibility have yet to be identified. The major problems in dealing with the gene expression dataset of autism include the presence of limited number of samples and large noises due to errors of experimental measurements and natural variation. In this study, a systematic combination of three important filters, namely t-test (TT), Wilcoxon Rank Sum (WRS) and Feature Correlation (COR) are applied along with efficient wrapper algorithm based on geometric binary particle swarm optimization-support vector machine (GBPSO-SVM), aiming at selecting and classifying the most attributed genes of autism. A new approach based on the criterion of median ratio, mean ratio and variance deviations is also applied to reduce the initial dataset prior to its involvement. Results showed that the most discriminative genes that were identified in the first and last selection steps concluded the presence of a repetitive gene (CAPS2), which was assigned as the most ASD risk gene. The fused result of genes subset that were selected by the GBPSO-SVM algorithm increased the classification accuracy to about 92.10%, which is higher than those reported in literature for the same autism dataset. Noticeably, the application of ensemble using random forest (RF) showed better performance compared to that of previous studies. However, the ensemble approach based on the employment of SVM as an integrator of the fused genes from the output branches of GBPSO-SVM outperformed the RF integrator. The overall improvement was ascribed to the selection strategies that were taken to reduce the dataset and the utilization of efficient wrapper based GBPSO-SVM algorithm