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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Feature Selection and Classification of Clinical Datasets Using Bioinspired Algorithms and Super Learner.

S Murugesan1, R S Bhuvaneswaran1, H Khanna Nehemiah1

  • 1Ramanujan Computing Centre, Anna University, Chennai 600025, India.

Computational and Mathematical Methods in Medicine
|May 31, 2021
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Summary
This summary is machine-generated.

A novel computer-aided diagnosis (CAD) system utilizes a super learner for disease detection. This system achieved high accuracy across multiple clinical datasets, demonstrating its diagnostic potential.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Accurate disease diagnosis is crucial for effective treatment.
  • Computer-aided diagnosis (CAD) systems offer potential for improving diagnostic accuracy and efficiency.

Purpose of the Study:

  • To develop and evaluate a novel computer-aided diagnosis (CAD) system employing a super learner for disease presence or absence diagnosis.
  • To assess the performance of bioinspired algorithms for feature selection in a CAD system.

Main Methods:

  • Feature selection was performed using three bioinspired algorithms: Cat Swarm Optimization (CSO), Krill Herd (KH), and Bacterial Foraging Optimization (BFO), with Support Vector Machine (SVM) accuracy as the fitness function.
  • Selected features were used to train three Back Propagation Neural Networks (BPNNs) independently.
  • A super learner was trained and tested using the classification results from the individual BPNNs.

Main Results:

  • The super learner achieved high classification accuracies across seven clinical datasets, including 96.83% for Wisconsin Diagnostic Breast Cancer (WDBC) and 94.74% for Hepatocellular Carcinoma (HCC).
  • Individual classifiers demonstrated varying performance, highlighting the benefit of the ensemble super learner approach.

Conclusions:

  • The developed super learner-based CAD system demonstrates significant potential for accurate disease diagnosis.
  • The integration of bioinspired algorithms for feature selection enhances the performance of the diagnostic system.