A Comprehensive Study on Machine Learning Methods to Increase the Prediction Accuracy of Classifiers and Reduce the Number of Medical Tests Required to Diagnose Alzheimer's Disease Article Swipe
Md. Sharifur Rahman
,
Girijesh Prasad
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5121/csit.2022.122107
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5121/csit.2022.122107
Alzheimer's patients gradually lose their ability to think, behave, and interact with others. Medical history, laboratory tests, daily activities, and personality changes can all be used to diagnose the disorder. A series of time-consuming and expensive tests are used to diagnose the illness. The most effective way to identify Alzheimer's disease is using a Random-forest classifier in this study, along with various other Machine Learning techniques. The main goal of this study is to fine-tune the classifier to detect illness with fewer tests while maintaining a reasonable disease discovery accuracy. We successfully identified the condition in almost 94% of cases using four of the thirty frequently utilized indicators.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- http://doi.org/10.5121/csit.2022.122107
- https://doi.org/10.5121/csit.2022.122107
- OA Status
- gold
- References
- 7
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4310238894
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4310238894Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5121/csit.2022.122107Digital Object Identifier
- Title
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A Comprehensive Study on Machine Learning Methods to Increase the Prediction Accuracy of Classifiers and Reduce the Number of Medical Tests Required to Diagnose Alzheimer's DiseaseWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-11-26Full publication date if available
- Authors
-
Md. Sharifur Rahman, Girijesh PrasadList of authors in order
- Landing page
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https://doi.org/10.5121/csit.2022.122107Publisher landing page
- PDF URL
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https://doi.org/10.5121/csit.2022.122107Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5121/csit.2022.122107Direct OA link when available
- Concepts
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Machine learning, Classifier (UML), Random forest, Artificial intelligence, Computer science, Disease, Alzheimer's disease, Personality, Psychology, Medicine, Pathology, Social psychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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7Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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