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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
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The center of gravity (COG) of an object is the point where the object's total weight is considered to be concentrated. Knowing the location of the center of gravity is useful when predicting the behavior of a moving object or designing static structures. In a uniform gravitational field, the center of gravity is similar to the center of mass (COM); yet, these two points can be positioned differently. For example, the Moon's center of mass lies very close to its geometric center, but...
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Related Experiment Video

Updated: Jan 26, 2026

Initial 3D Cell Cluster Control in a Hybrid Gel Cube Device for Repeatable Pattern Formations
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A novel optimized initial cluster center and enhanced objective function: Medical diagnosis through classification.

Binay Subedi, Abeer Alsadoon, Pwc Prasad1

  • 1Charles Sturt University, Australia.

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|April 12, 2019
PubMed
Summary

This study enhances medical diagnosis by improving multilabel classification accuracy for conditions like high blood pressure and diabetes. The new technique boosts accuracy by 5-7% and reduces processing time, aiding doctors in patient diagnosis.

Keywords:
feature extractionfuzzy C-meansknowledge discoverymultilabel classificationoverlapping clustering

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Area of Science:

  • Medical Informatics
  • Machine Learning

Background:

  • Medical datasets are often multilabel, meaning patients can have multiple conditions simultaneously.
  • Accurate classification is crucial for effective medical diagnosis.

Purpose of the Study:

  • To enhance the accuracy and performance of multilabel classification for medical diagnosis.
  • To introduce an improved overlapping clustering method combined with multilabel feature selection.

Main Methods:

  • The proposed system utilizes Optimized Initial Cluster Centers and an Enhanced Objective Function for clustering.
  • Multilabel feature selection and an improved overlapping clustering technique are employed.

Main Results:

  • The proposed technique demonstrated superiority over existing methods across 10 datasets.
  • Classification accuracy improved by an average of 5%-7%.
  • Processing time was reduced by 0.5-1 second on average.

Conclusions:

  • The developed system offers improved accuracy and processing time for multilabel classification in medical diagnosis.
  • This advancement assists healthcare professionals in diagnosing complex patient conditions more efficiently.