Real-Time Jaundice Detection in Neonates Based on Machine Learning Models Article Swipe
Ahmad Yaseen Abdulrazzak
,
Saleem Latif Mohammed
,
Ali Al‐Naji
,
Javaan Chahl
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/biomedinformatics4010034
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/biomedinformatics4010034
Introduction: Despite the many attempts made by researchers to diagnose jaundice non-invasively using machine learning techniques, the low amount of data used to build their models remains the key factor limiting the performance of their models. Objective: To build a system to diagnose neonatal jaundice non-invasively based on machine learning algorithms created based on a dataset comprising 767 infant images using a computer device and a USB webcam. Methods: The first stage of the proposed system was to evaluate the performance of four machine learning algorithms, namely support vector machine (SVM), k nearest neighbor (k-NN), random forest (RF), and extreme gradient boost (XGBoost), based on a dataset of 767 infant images. The algorithm with the best performance was chosen as the classifying algorithm in the developed application. The second stage included designing an application that enables the user to perform jaundice detection for a patient under test with the minimum effort required by capturing the patient’s image using a USB webcam. Results: The obtained results of the first stage of the machine learning algorithms evaluation process indicated that XGBoost outperformed the rest of the algorithms by obtaining an accuracy of 99.63%. The second-best algorithm was the RF algorithm, which had an accuracy of 98.99%. Following RF, with a slight difference, was the k-NN algorithm. It achieved an accuracy of 98.25%. SVM scored the lowest performance among the above three algorithms, with an accuracy of 96.22%. Based on these obtained results, the XGBoost algorithm was chosen to be the classifier of the proposed system. In the second stage, the jaundice application was designed based on the model created by the XGBoost algorithm. This application ensured it was user friendly with as fast a processing time as possible. Conclusion: Early detection of neonatal jaundice is crucial due to the severity of its complications. A non-invasive system using a USB webcam and an XGBoost machine learning technique was proposed. The XGBoost algorithm achieved 99.63% accuracy and successfully diagnosed 10 out of 10 NICU infants with very little processing time. This denotes the efficiency of machine learning algorithms in healthcare in general and in monitoring systems specifically.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/biomedinformatics4010034
- https://www.mdpi.com/2673-7426/4/1/34/pdf?version=1708767028
- OA Status
- diamond
- Cited By
- 16
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392162056
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4392162056Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/biomedinformatics4010034Digital Object Identifier
- Title
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Real-Time Jaundice Detection in Neonates Based on Machine Learning ModelsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-24Full publication date if available
- Authors
-
Ahmad Yaseen Abdulrazzak, Saleem Latif Mohammed, Ali Al‐Naji, Javaan ChahlList of authors in order
- Landing page
-
https://doi.org/10.3390/biomedinformatics4010034Publisher landing page
- PDF URL
-
https://www.mdpi.com/2673-7426/4/1/34/pdf?version=1708767028Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://www.mdpi.com/2673-7426/4/1/34/pdf?version=1708767028Direct OA link when available
- Concepts
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Jaundice, Computer science, Artificial intelligence, Machine learning, Medicine, Internal medicineTop concepts (fields/topics) attached by OpenAlex
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16Total citation count in OpenAlex
- Citations by year (recent)
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2025: 11, 2024: 5Per-year citation counts (last 5 years)
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34Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.rest | 181 |
| abstract_inverted_index.test | 146 |
| abstract_inverted_index.that | 134, 177 |
| abstract_inverted_index.time | 283 |
| abstract_inverted_index.used | 21 |
| abstract_inverted_index.user | 137, 276 |
| abstract_inverted_index.very | 332 |
| abstract_inverted_index.with | 113, 147, 206, 230, 278, 331 |
| abstract_inverted_index.(RF), | 97 |
| abstract_inverted_index.Based | 235 |
| abstract_inverted_index.Early | 287 |
| abstract_inverted_index.above | 227 |
| abstract_inverted_index.among | 225 |
| abstract_inverted_index.based | 46, 52, 103, 262 |
| abstract_inverted_index.boost | 101 |
| abstract_inverted_index.build | 23, 38 |
| abstract_inverted_index.first | 70, 167 |
| abstract_inverted_index.image | 156 |
| abstract_inverted_index.model | 265 |
| abstract_inverted_index.stage | 71, 129, 168 |
| abstract_inverted_index.their | 24, 34 |
| abstract_inverted_index.these | 237 |
| abstract_inverted_index.three | 228 |
| abstract_inverted_index.time. | 335 |
| abstract_inverted_index.under | 145 |
| abstract_inverted_index.using | 12, 60, 157, 304 |
| abstract_inverted_index.which | 198 |
| abstract_inverted_index.(SVM), | 90 |
| abstract_inverted_index.99.63% | 320 |
| abstract_inverted_index.amount | 18 |
| abstract_inverted_index.chosen | 118, 244 |
| abstract_inverted_index.device | 63 |
| abstract_inverted_index.effort | 150 |
| abstract_inverted_index.factor | 29 |
| abstract_inverted_index.forest | 96 |
| abstract_inverted_index.images | 59 |
| abstract_inverted_index.infant | 58, 109 |
| abstract_inverted_index.little | 333 |
| abstract_inverted_index.lowest | 223 |
| abstract_inverted_index.models | 25 |
| abstract_inverted_index.namely | 86 |
| abstract_inverted_index.random | 95 |
| abstract_inverted_index.scored | 221 |
| abstract_inverted_index.second | 128, 255 |
| abstract_inverted_index.slight | 208 |
| abstract_inverted_index.stage, | 256 |
| abstract_inverted_index.system | 40, 75, 303 |
| abstract_inverted_index.vector | 88 |
| abstract_inverted_index.webcam | 307 |
| abstract_inverted_index.(k-NN), | 94 |
| abstract_inverted_index.96.22%. | 234 |
| abstract_inverted_index.98.25%. | 219 |
| abstract_inverted_index.98.99%. | 203 |
| abstract_inverted_index.99.63%. | 190 |
| abstract_inverted_index.Despite | 1 |
| abstract_inverted_index.XGBoost | 178, 241, 269, 310, 317 |
| abstract_inverted_index.created | 51, 266 |
| abstract_inverted_index.crucial | 293 |
| abstract_inverted_index.dataset | 55, 106 |
| abstract_inverted_index.denotes | 337 |
| abstract_inverted_index.enables | 135 |
| abstract_inverted_index.ensured | 273 |
| abstract_inverted_index.extreme | 99 |
| abstract_inverted_index.general | 347 |
| abstract_inverted_index.images. | 110 |
| abstract_inverted_index.infants | 330 |
| abstract_inverted_index.machine | 13, 48, 83, 89, 171, 311, 341 |
| abstract_inverted_index.minimum | 149 |
| abstract_inverted_index.models. | 35 |
| abstract_inverted_index.nearest | 92 |
| abstract_inverted_index.patient | 144 |
| abstract_inverted_index.perform | 139 |
| abstract_inverted_index.process | 175 |
| abstract_inverted_index.remains | 26 |
| abstract_inverted_index.results | 164 |
| abstract_inverted_index.support | 87 |
| abstract_inverted_index.system. | 252 |
| abstract_inverted_index.systems | 351 |
| abstract_inverted_index.webcam. | 67, 160 |
| abstract_inverted_index.Methods: | 68 |
| abstract_inverted_index.Results: | 161 |
| abstract_inverted_index.accuracy | 188, 201, 217, 232, 321 |
| abstract_inverted_index.achieved | 215, 319 |
| abstract_inverted_index.attempts | 4 |
| abstract_inverted_index.computer | 62 |
| abstract_inverted_index.designed | 261 |
| abstract_inverted_index.diagnose | 9, 42 |
| abstract_inverted_index.evaluate | 78 |
| abstract_inverted_index.friendly | 277 |
| abstract_inverted_index.gradient | 100 |
| abstract_inverted_index.included | 130 |
| abstract_inverted_index.jaundice | 10, 44, 140, 258, 291 |
| abstract_inverted_index.learning | 14, 49, 84, 172, 312, 342 |
| abstract_inverted_index.limiting | 30 |
| abstract_inverted_index.neighbor | 93 |
| abstract_inverted_index.neonatal | 43, 290 |
| abstract_inverted_index.obtained | 163, 238 |
| abstract_inverted_index.proposed | 74, 251 |
| abstract_inverted_index.required | 151 |
| abstract_inverted_index.results, | 239 |
| abstract_inverted_index.severity | 297 |
| abstract_inverted_index.Following | 204 |
| abstract_inverted_index.algorithm | 112, 122, 193, 242, 318 |
| abstract_inverted_index.capturing | 153 |
| abstract_inverted_index.designing | 131 |
| abstract_inverted_index.detection | 141, 288 |
| abstract_inverted_index.developed | 125 |
| abstract_inverted_index.diagnosed | 324 |
| abstract_inverted_index.indicated | 176 |
| abstract_inverted_index.obtaining | 186 |
| abstract_inverted_index.possible. | 285 |
| abstract_inverted_index.proposed. | 315 |
| abstract_inverted_index.technique | 313 |
| abstract_inverted_index.(XGBoost), | 102 |
| abstract_inverted_index.Objective: | 36 |
| abstract_inverted_index.algorithm, | 197 |
| abstract_inverted_index.algorithm. | 213, 270 |
| abstract_inverted_index.algorithms | 50, 173, 184, 343 |
| abstract_inverted_index.classifier | 248 |
| abstract_inverted_index.comprising | 56 |
| abstract_inverted_index.efficiency | 339 |
| abstract_inverted_index.evaluation | 174 |
| abstract_inverted_index.healthcare | 345 |
| abstract_inverted_index.monitoring | 350 |
| abstract_inverted_index.processing | 282, 334 |
| abstract_inverted_index.Conclusion: | 286 |
| abstract_inverted_index.algorithms, | 85, 229 |
| abstract_inverted_index.application | 133, 259, 272 |
| abstract_inverted_index.classifying | 121 |
| abstract_inverted_index.difference, | 209 |
| abstract_inverted_index.patient’s | 155 |
| abstract_inverted_index.performance | 32, 80, 116, 224 |
| abstract_inverted_index.researchers | 7 |
| abstract_inverted_index.second-best | 192 |
| abstract_inverted_index.techniques, | 15 |
| abstract_inverted_index.application. | 126 |
| abstract_inverted_index.non-invasive | 302 |
| abstract_inverted_index.outperformed | 179 |
| abstract_inverted_index.successfully | 323 |
| abstract_inverted_index.Introduction: | 0 |
| abstract_inverted_index.specifically. | 352 |
| abstract_inverted_index.complications. | 300 |
| abstract_inverted_index.non-invasively | 11, 45 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5000023371 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I170239107, https://openalex.org/I4210115078 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.5199999809265137 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.98176798 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |