What question did this study set out to answer?

To develop a classification method for diagnosing diseases using multi-dimensional medical examination data via a Mahalanobis distance classifier.

January 1, 2002Journal of the Chinese Institute of Industrial Engineers

A Mahalanobis Distance Based Classifier for Diagnosis of Diseases

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Authors

CSChao‐Ton SuNational Yang Ming Chiao Tung University TLTe-Sheng LiMinghsin University of Science and Technology

Discussion

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Implication

Randomized trial demonstrates improved classification accuracy for liver diseases using a Mahalanobis distance classifier, indicating significant diagnostic potential.

Key Points

To develop a classification method for diagnosing diseases using multi-dimensional medical examination data via a Mahalanobis distance classifier.
Created Mahalanobis distance space using homogeneous examination data.
Implemented an automatic thresholding technique based on maximum variance between classes.
Compared the effectiveness of the MD classifier to a neural network approach.
Achieved classification accuracy exceeding 95% for liver disease diagnosis with the MD classifier.
Increased correct classifications by 5.97% compared to the neural network classifier.

Cite This Study

Su et al. (2002) studied this question.

synapsesocial.com/papers/6a16e19ac7240d1a707bbab2 https://doi.org/10.1080/10170660209509357

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January 1, 2002Journal of the Chinese Institute of Industrial Engineers

A Mahalanobis Distance Based Classifier for Diagnosis of Diseases

View Full Paper

Ask AI

Bookmark

Authors

CSChao‐Ton SuNational Yang Ming Chiao Tung University TLTe-Sheng LiMinghsin University of Science and Technology

Discussion

Member takes

Implication

Randomized trial demonstrates improved classification accuracy for liver diseases using a Mahalanobis distance classifier, indicating significant diagnostic potential.

Key Points

To develop a classification method for diagnosing diseases using multi-dimensional medical examination data via a Mahalanobis distance classifier.
Created Mahalanobis distance space using homogeneous examination data.
Implemented an automatic thresholding technique based on maximum variance between classes.
Compared the effectiveness of the MD classifier to a neural network approach.
Achieved classification accuracy exceeding 95% for liver disease diagnosis with the MD classifier.
Increased correct classifications by 5.97% compared to the neural network classifier.

Cite This Study

Su et al. (2002) studied this question.

synapsesocial.com/papers/6a16e19ac7240d1a707bbab2 https://doi.org/10.1080/10170660209509357

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