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A Disease Diagnosis Method Based on Machine Learning

Published: 19 May 2018 Publication History

Abstract

For the problem of low accuracy of intelligent medical diagnosis, this paper designs a secondary diagnosis model based on machine learning. First diagnosis using softmax regression model. It will conduct a diagnosis of major disease classification, greatly reducing the scope of diagnosis. The second diagnosis uses Naive Bayesian network for accurate diagnosis. The combination of the two methods improves the diagnostic accuracy. At the same time, the model training time is reduced as much as possible. According to the experimental results, the model can diagnose the condition more accurately. The diagnostic accuracy of the model is relatively satisfactory. It can provide a good definitive reference for doctors.

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Cited By

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  • (2023)A Pilot Study: Detrusor Overactivity Diagnosis Method Based on Deep LearningUrology10.1016/j.urology.2023.04.030179(188-195)Online publication date: Sep-2023
  • (2021)Medical Prescription and Report AnalyzerProceedings of the 2021 Thirteenth International Conference on Contemporary Computing10.1145/3474124.3474165(286-295)Online publication date: 5-Aug-2021

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cover image ACM Other conferences
ICIIP '18: Proceedings of the 3rd International Conference on Intelligent Information Processing
May 2018
249 pages
ISBN:9781450364966
DOI:10.1145/3232116
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

  • Guilin: Guilin University of Technology, Guilin, China
  • International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 May 2018

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Author Tags

  1. intelligent diagnosis
  2. naive Bayes
  3. secondary diagnosis
  4. softmax regression

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ICIIP '18

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Overall Acceptance Rate 87 of 367 submissions, 24%

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Cited By

View all
  • (2023)A Pilot Study: Detrusor Overactivity Diagnosis Method Based on Deep LearningUrology10.1016/j.urology.2023.04.030179(188-195)Online publication date: Sep-2023
  • (2021)Medical Prescription and Report AnalyzerProceedings of the 2021 Thirteenth International Conference on Contemporary Computing10.1145/3474124.3474165(286-295)Online publication date: 5-Aug-2021

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