English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the field of health-care and bio-medical research, understanding therelationship between the symptoms of diseases is crucial for early diagnosisand determining hidden relationships between diseases. The study aimed tounderstand the extent of symptom types in disease prediction tasks. In thisresearch, we analyze a pre-generated symptom-based human disease dataset anddemonstrate the degree of predictability for each disease based on theConvolutional Neural Network and the Support Vector Machine. Ambiguity ofdisease is studied using the K-Means and the Principal Component Analysis. Ourresults indicate that machine learning can potentially diagnose diseases withthe 98-100% accuracy in the early stage, taking the characteristics of symptomsinto account. Our result highlights that types of unusual symptoms are a goodproxy for disease early identification accurately. We also highlight thatunusual symptoms increase the accuracy of the disease prediction task.

Explorative analysis of human disease-symptoms relations using the Convolutional Neural Network