A comprehensive search for expert classification methods in disease diagnosis and prediction

2018 ◽  
Vol 36 (1) ◽  
pp. e12343 ◽  
Author(s):  
Sunil Kr. Jha ◽  
Zhaoqing Pan ◽  
Ehsan Elahi ◽  
Nilesh Patel
Keyword(s):  
Disease Diagnosis ◽  
Classification Methods ◽  
Comprehensive Search ◽  
Expert Classification

scholarly journals dbSUPER: a database of super-enhancers in mouse and human genome

2015 ◽  
Author(s):  
Aziz Khan ◽  
Xuegong Zhang
Keyword(s):  
Transcriptional Control ◽  
Genetic Research ◽  
Cell Types ◽  
Disease Diagnosis ◽  
Specific Gene ◽  
Analysis Tool ◽  
Cell Identity ◽  
Super Enhancer ◽  
Comprehensive Search ◽  
Downstream Analysis

Super-enhancers are the clusters of transcriptional enhancers that can drive cell-type-specific gene expression and also crucial in cell identity. Many disease-associated sequence variations are enriched in super-enhancer regions of disease-relevant cell types. Thus, super-enhancers can be used as potential biomarkers for disease diagnosis and therapeutics. Current studies have identified super-enhancers in more than 100 cell types and demonstrated their functional importance. However, no centralized resource to integrate all these findings is available yet. We developed dbSUPER (http://bioinfo.au.tsinghua.edu.cn/dbsuper/), the first integrated and interactive database of super-enhancers, with the primary goal of providing a resource for assistance in further studies related to transcriptional control of cell identity and disease. dbSUPER provides a responsive and user-friendly web interface to facilitate efficient and comprehensive search and browsing. The data can be easily sent to Galaxy instances, GREAT and Cistrome web servers for downstream analysis, and can be visualized in UCSC genome browser while custom tracks added automatically. The data can be downloaded and exported in variety of formats. Further, dbSUPER lists genes associated with the super-enhancers and links to various other databases such as GeneCards, UniProt and Entrez. dbSUPER also provides an overlap analysis tool, to annotate user defined regions. We believe dbSUPER is a valuable resource for the biologists and genetic research communities.

Effectiveness evaluation of expert classification methods

2002 ◽  
Vol 138 (2) ◽  
pp. 260-273 ◽  
Author(s):  
Oleg Larichev ◽  
Artyom Asanov ◽  
Yevgeny Naryzhny
Keyword(s):  
Effectiveness Evaluation ◽  
Classification Methods ◽  
Expert Classification

scholarly journals Machine Learning Approach for Classification of Cancer Stages

2019 ◽  
pp. 1-4
Author(s):  
Shubham Hingmire
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Health Care ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Classification Methods ◽  
Medical Diagnoses ◽  
Machine Learning Approach ◽  
Computer Aided

The simplest form of health care is diagnosis and prevention. of disease. Machine learning (ML) methods help achieve this goal. This project aims to compare method of computer aided medical diagnoses. The ?rst of these methods is a classify disease diagnosis according to their data. This involves the training of an Arti?cial Neural Network to respond to several patient parameters. And also comparing various classification methods the purpose research classifier classi?es the patients in two class ?rst is malignant and second is benign.

scholarly journals Exploiting of Classification Paradigms for Early diagnosis of Alzheimer’s disease

2021 ◽  
Vol 9 (2) ◽  
pp. 281-288
Author(s):  
G Stalin Babu, Et. al.
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Feature Extraction ◽  
Early Diagnosis ◽  
Disease Diagnosis ◽  
Classification Methods ◽  
Brain Images ◽  
Deep Convolution Neural Network

Alzheimer’s disorder is an incurable neurodegenerative disease that ordinarily affects the aged population. Coherent automated assessment methods are essential for Alzheimer's disease diagnosis in early from distinct images modalities using Machine Learning. This article focuses on exploring various feature extraction and classification methods for early detection of AD proposed by researchers and proposes a modern predictive model that includes Voxel based Texture analysis of brain images for extract features and Optimized Classifier Deep Convolution Neural Network (DCNN) employed for enhance accuracy.

scholarly journals Patients’ perspectives on Bronchiectasis: findings from a social media listening (SML) study

2021 ◽  
pp. 00096-2021
Author(s):  
Irisz Delestre-Levai ◽  
Stefano Aliberti ◽  
Marta Almagro ◽  
Chiara Carnini ◽  
James D. Chalmers ◽  
...  
Keyword(s):  
Social Media ◽  
Age Groups ◽  
Disease Diagnosis ◽  
Emotional Impact ◽  
Mucus Production ◽  
Social Media Platforms ◽  
Comprehensive Search ◽  
Media Channels ◽  
Traditional Approaches

Although it is of great importance for healthcare professionals to ensure that patients’ needs and concerns are valued and that they feel confident in the quality of the care they receive, there have been few studies specifically addressing the opinions, experiences and needs of patients with Bronchiectasis (BE), and more importantly the emotional impact of the disease, diagnosis and treatment.Using enterprise grade social listening tools, a comprehensive search around BE was performed in 5 languages, on different social media platforms between January 2018 and December 2019 to obtain the perspectives of patients and caregivers from 9 countries on symptoms, treatments and burden of the disease.Over 27 000 mentions of BE were identified on social media channels, 38.8% of which were posted by patients and caregivers. Approximately 1600 posts were found on BE symptoms, out of which persistent cough, shortness of breath and mucus production (22%, 20% and 18%, respectively) were the most commonly discussed. The research revealed that existing diagnostic tests often delay diagnosis or provide inaccurate results, leading to multiple rounds of consults and substantial delays in treatment initiation and management of the disease. Misdiagnosis was common across different age groups, especially among patients without severe symptoms and this was associated with an emotional burden of anger, confusion, frustration and anxiety.Analysis of social media presents a new approach to derive insights on patients’ experiences and emotions with BE and has the potential to complement more traditional approaches to drive more patient-focused drug development.

Classification of Diabetes by Kernel based SVM with PSO

2019 ◽  
Vol 12 ◽  
Author(s):  
Dilip Kumar Choubey ◽  
Sudhakar Tripathi ◽  
Prabhat Kumar ◽  
Vaibhav Shukla ◽  
Vinay Kumar Dhandhania
Keyword(s):  
Computation Time ◽  
Research Paper ◽  
Disease Diagnosis ◽  
Feature Reduction ◽  
Classification Method ◽  
Polynomial Kernel ◽  
Second Phase ◽  
Sigmoid Function ◽  
Classification Methods ◽  
Kernel Svm

Background: Classification method is needed to deduce the possible errors and assist the doctor’s. These methods are used in every many of our lives to take suitable decisions. It is well known that classification is an efficient, effective and broadly utilized strategy in several applications such as medical disease diagnosis, etc. The prime objective of this research paper is to achieve an efficient and effective classification method for Diabetes. Discussion: The proposed methodology comprises of two phases: The first phase deals with description of Pima Indian Diabetes Dataset and Localized Diabetes Dataset whereas in the second phase dataset has been processed through two different approaches. First approach entails classification through Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel and Linear Kernel SVM on Pima Indian Diabetes Dataset and Localized Diabetes Dataset. In the second approach, PSO have been utilized as a feature reduction method followed by using the same set of classification methods used in the first approach. PSO_Linear Kernel SVM provides the highest accuracy and ROC for both the above mentioned dataset. Conclusion: In this research paper, comparative analysis of outcomes w.r.t. performance assessment has been done using both with PSO and without PSO for the same set of classification methods. Finally, it has been concluded that PSO is selecting the relevant features, reducing the expense and computation time while improving the ROC and accuracy. The used methodology may similarly be implemented in other medical diseases.

Use of TEM in Plant Disease Diagnosis: A Xylem-Limited Bacterium Associated with Diseased ‘Toronto’ Creeping Bentgrass

1981 ◽  
Vol 39 ◽  
pp. 414-415
Author(s):  
Karen K. Baker ◽  
David L. Roberts
Keyword(s):  
Osmium Tetroxide ◽  
Plant Disease ◽  
Leaf Tissue ◽  
Infectious Agent ◽  
Creeping Bentgrass ◽  
Disease Diagnosis ◽  
Unknown Etiology ◽  
Agrostis Palustris ◽  
Putting Greens ◽  
Plant Disease Diagnosis

Plant disease diagnosis is most often accomplished by examination of symptoms and observation or isolation of causal organisms. Occasionally, diseases of unknown etiology occur and are difficult or impossible to accurately diagnose by the usual means. In 1980, such a disease was observed on Agrostis palustris Huds. c.v. Toronto (creeping bentgrass) putting greens at the Butler National Golf Course in Oak Brook, IL.The wilting symptoms of the disease and the irregular nature of its spread through affected areas suggested that an infectious agent was involved. However, normal isolation procedures did not yield any organism known to infect turf grass. TEM was employed in order to aid in the possible diagnosis of the disease.Crown, root and leaf tissue of both infected and symptomless plants were fixed in cold 5% glutaraldehyde in 0.1 M phosphate buffer, post-fixed in buffered 1% osmium tetroxide, dehydrated in ethanol and embedded in a 1:1 mixture of Spurrs and epon-araldite epoxy resins.

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