A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient Health Article Swipe
Md. Moddassir Alam
,
Md. Mottahir Alam
,
Muhammad Moinuddin
,
Mohammad Tauheed Ahmad
,
Jabir Hakami
,
Anis Ahmad Chaudhary
,
Asif Irshad Khan
,
Tauheed Khan Mohd
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2023.032118
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.32604/cmc.2023.032118
Artificial Intelligence (AI) is finding increasing application in healthcare monitoring. Machine learning systems are utilized for monitoring patient health through the use of IoT sensor, which keep track of the physiological state by way of various health data. Thus, early detection of any disease or derangement can aid doctors in saving patients’ lives. However, there are some challenges associated with predicting health status using the common algorithms, such as time requirements, chances of errors, and improper classification. We propose an Artificial Krill Herd based on the Random Forest (AKHRF) technique for monitoring patients’ health and eliciting an optimal prescription based on their health status. To begin with, various patient datasets were collected and trained into the system using IoT sensors. As a result, the framework developed includes four processes: preprocessing, feature extraction, classification, and result visibility. Additionally, preprocessing removes errors, noise, and missing values from the dataset, whereas feature extraction extracts the relevant information. Then, in the classification layer, we updated the fitness function of the krill herd to classify the patient’s health status and also generate a prescription. We found that the results from the proposed framework are comparable to the results from other state-of-the-art techniques in terms of sensitivity, specificity, Area under the Curve (AUC), accuracy, precision, recall, and F-measure.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.32604/cmc.2023.032118
- https://www.techscience.com/cmc/v75n2/52025/pdf
- OA Status
- diamond
- Cited By
- 2
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4362015348
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4362015348Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.32604/cmc.2023.032118Digital Object Identifier
- Title
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A Novel Krill Herd Based Random Forest Algorithm for Monitoring Patient HealthWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-01-01Full publication date if available
- Authors
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Md. Moddassir Alam, Md. Mottahir Alam, Muhammad Moinuddin, Mohammad Tauheed Ahmad, Jabir Hakami, Anis Ahmad Chaudhary, Asif Irshad Khan, Tauheed Khan MohdList of authors in order
- Landing page
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https://doi.org/10.32604/cmc.2023.032118Publisher landing page
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https://www.techscience.com/cmc/v75n2/52025/pdfDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://www.techscience.com/cmc/v75n2/52025/pdfDirect OA link when available
- Concepts
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Random forest, Artificial intelligence, Preprocessor, Machine learning, Computer science, Feature extraction, Statistical classification, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2024: 1, 2023: 1Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.developed | 125 |
| abstract_inverted_index.eliciting | 95 |
| abstract_inverted_index.framework | 124, 187 |
| abstract_inverted_index.technique | 89 |
| abstract_inverted_index.Artificial | 0, 80 |
| abstract_inverted_index.F-measure. | 211 |
| abstract_inverted_index.associated | 58 |
| abstract_inverted_index.challenges | 57 |
| abstract_inverted_index.comparable | 189 |
| abstract_inverted_index.extraction | 149 |
| abstract_inverted_index.healthcare | 8 |
| abstract_inverted_index.increasing | 5 |
| abstract_inverted_index.monitoring | 16, 91 |
| abstract_inverted_index.precision, | 208 |
| abstract_inverted_index.predicting | 60 |
| abstract_inverted_index.processes: | 128 |
| abstract_inverted_index.techniques | 196 |
| abstract_inverted_index.algorithms, | 66 |
| abstract_inverted_index.application | 6 |
| abstract_inverted_index.derangement | 45 |
| abstract_inverted_index.extraction, | 131 |
| abstract_inverted_index.monitoring. | 9 |
| abstract_inverted_index.patients’ | 51, 92 |
| abstract_inverted_index.patient’s | 171 |
| abstract_inverted_index.visibility. | 135 |
| abstract_inverted_index.Intelligence | 1 |
| abstract_inverted_index.information. | 153 |
| abstract_inverted_index.prescription | 98 |
| abstract_inverted_index.sensitivity, | 200 |
| abstract_inverted_index.specificity, | 201 |
| abstract_inverted_index.Additionally, | 136 |
| abstract_inverted_index.physiological | 30 |
| abstract_inverted_index.preprocessing | 137 |
| abstract_inverted_index.prescription. | 178 |
| abstract_inverted_index.requirements, | 70 |
| abstract_inverted_index.classification | 157 |
| abstract_inverted_index.preprocessing, | 129 |
| abstract_inverted_index.classification, | 132 |
| abstract_inverted_index.classification. | 76 |
| abstract_inverted_index.state-of-the-art | 195 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.65098531 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |