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Related Concept Videos

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
Statistical Analysis System (SAS)01:14

Statistical Analysis System (SAS)

SAS, short for Statistical Analysis System, is a powerful data analysis, management, and visualization tool. Developed by the SAS Institute in the early 1970s, SAS has evolved into a comprehensive software suite used across various industries for statistical analysis, business intelligence, and predictive modeling.
Applications: SAS finds applications in numerous fields, including healthcare for clinical trial analysis, finance for risk assessment, marketing for customer data analysis, and...
Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...

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Related Experiment Video

Updated: Jun 29, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Selection of examples in case-based computer-aided decision systems.

Maciej A Mazurowski1, Jacek M Zurada, Georgia D Tourassi

  • 1Department of Electrical and Computer Engineering, University of Louisville, Lutz Hall, Room 407, Louisville, KY 40292, USA. maciej.mazurowski@louisville.edu

Physics in Medicine and Biology
|October 16, 2008
PubMed
Summary
This summary is machine-generated.

Intelligent techniques for case-based computer-aided decision (CB-CAD) systems can significantly reduce evidence database size while improving diagnostic performance. Random mutation hill climbing offers the best balance for building efficient CB-CAD systems.

Related Experiment Videos

Last Updated: Jun 29, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Machine Learning for Healthcare

Background:

  • Case-based computer-aided decision (CB-CAD) systems leverage historical data for classification.
  • Adaptive CB-CAD systems are suitable for expanding medical digital databases.
  • Efficient management of evidence databases is crucial for CB-CAD system performance.

Purpose of the Study:

  • To investigate intelligent techniques for optimizing CB-CAD evidence databases.
  • To evaluate genetic algorithm-based selection, greedy selection, and random mutation hill climbing.
  • To compare these techniques against random selection for false positive reduction in mammography.

Main Methods:

  • Implemented and evaluated three intelligent selection strategies: genetic algorithm, greedy, and random mutation hill climbing.
  • Utilized a previously developed CB-CAD system for false positive reduction in screening mammograms.
  • Compared the performance and database size reduction achieved by each technique against a random selection baseline.

Main Results:

  • Intelligent techniques reduced evidence database size to 37% while improving diagnostic performance.
  • Database size was reduced to 2-4% without compromising diagnostic performance when size was the primary concern.
  • Random mutation hill climbing demonstrated the optimal balance between diagnostic performance and computational efficiency.

Conclusions:

  • Intelligent database management significantly enhances CB-CAD system efficiency and diagnostic accuracy.
  • Effective selection strategies can drastically reduce storage requirements for medical decision support systems.
  • Random mutation hill climbing is recommended for building efficient and high-performing CB-CAD evidence databases.