Clustering and Machine Learning Models of Skeletal Class I and II Parameters of Arab Orthodontic Patients Article Swipe
Kareem Midlej
,
Osayd Zohud
,
Iqbal M. Lone
,
Obaida Awadi
,
Samir Masarwa
,
Eva Paddenberg
,
Sebastian Krohn
,
Christian Kirschneck
,
Peter Proff
,
Nezar Watted
,
Fuad A. Iraqi
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202412.2108.v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202412.2108.v1
Background: The main aim of this study is to introduce new classification methods for skeletal class I occlusion (SCIO) and skeletal class II malocclusion (SCIIMO) among Arab patients in Israel. We conducted hierarchical clustering to detect critical trends within malocclusion classes and applied machine-learning (ML) models to predict classification outcomes. Methods: This study is based on assessing the lateral cephalometric parameters from the XXXX center. The study consisted of the coded records of 394 Arab patients who were diagnosed as SCIO/SCIIMO, according to the individualized ANB of Panagiotidis and Witt. After clustering analysis, an ML model was established by evaluating the performance of different models. Results: The clustering analysis identified three distinct clusters for each skeletal class. The clusters variated in the degree of retrognathism and the vertical facial growth pattern, representing significant differences in the parameters ANB angle, Calculated_ANB, and gonial angle. Besides, SCIIMO clusters revealed substantial age differences between the different clusters. The general ML model that included all parameters to classify the patients showed an accuracy of 0.87 in the random forest and the Classification and Regression Tree. Using ANB angle and Wits appraisal only in the ML, an Accuracy of 0.78 (Sensitivity=0.80, Specificity=0.76) was achieved to classify patients as SCIO or SCIIMO. Conclusion: The clustering analysis revealed distinguished patterns that can be present within SCIO and SCIIMO patients, which can affect the diagnosis and treatment plan. In addition, the ML model can accurately diagnose SCIO/SCIIMO patients, which should improve precise diagnostics.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202412.2108.v1
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4405782457
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4405782457Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20944/preprints202412.2108.v1Digital Object Identifier
- Title
-
Clustering and Machine Learning Models of Skeletal Class I and II Parameters of Arab Orthodontic PatientsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-12-24Full publication date if available
- Authors
-
Kareem Midlej, Osayd Zohud, Iqbal M. Lone, Obaida Awadi, Samir Masarwa, Eva Paddenberg, Sebastian Krohn, Christian Kirschneck, Peter Proff, Nezar Watted, Fuad A. IraqiList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202412.2108.v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.20944/preprints202412.2108.v1Direct OA link when available
- Concepts
-
Cluster analysis, Class (philosophy), Computer science, Machine learning, Artificial intelligence, Data miningTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4405782457 |
|---|---|
| doi | https://doi.org/10.20944/preprints202412.2108.v1 |
| ids.doi | https://doi.org/10.20944/preprints202412.2108.v1 |
| ids.openalex | https://openalex.org/W4405782457 |
| fwci | 0.0 |
| type | preprint |
| title | Clustering and Machine Learning Models of Skeletal Class I and II Parameters of Arab Orthodontic Patients |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T11363 |
| topics[0].field.id | https://openalex.org/fields/35 |
| topics[0].field.display_name | Dentistry |
| topics[0].score | 0.9412000179290771 |
| topics[0].domain.id | https://openalex.org/domains/4 |
| topics[0].domain.display_name | Health Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/3504 |
| topics[0].subfield.display_name | Oral Surgery |
| topics[0].display_name | Dental Radiography and Imaging |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C73555534 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7665114998817444 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q622825 |
| concepts[0].display_name | Cluster analysis |
| concepts[1].id | https://openalex.org/C2777212361 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5986740589141846 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q5127848 |
| concepts[1].display_name | Class (philosophy) |
| concepts[2].id | https://openalex.org/C41008148 |
| concepts[2].level | 0 |
| concepts[2].score | 0.49717119336128235 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[2].display_name | Computer science |
| concepts[3].id | https://openalex.org/C119857082 |
| concepts[3].level | 1 |
| concepts[3].score | 0.44396719336509705 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q2539 |
| concepts[3].display_name | Machine learning |
| concepts[4].id | https://openalex.org/C154945302 |
| concepts[4].level | 1 |
| concepts[4].score | 0.41305097937583923 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[4].display_name | Artificial intelligence |
| concepts[5].id | https://openalex.org/C124101348 |
| concepts[5].level | 1 |
| concepts[5].score | 0.33082032203674316 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[5].display_name | Data mining |
| keywords[0].id | https://openalex.org/keywords/cluster-analysis |
| keywords[0].score | 0.7665114998817444 |
| keywords[0].display_name | Cluster analysis |
| keywords[1].id | https://openalex.org/keywords/class |
| keywords[1].score | 0.5986740589141846 |
| keywords[1].display_name | Class (philosophy) |
| keywords[2].id | https://openalex.org/keywords/computer-science |
| keywords[2].score | 0.49717119336128235 |
| keywords[2].display_name | Computer science |
| keywords[3].id | https://openalex.org/keywords/machine-learning |
| keywords[3].score | 0.44396719336509705 |
| keywords[3].display_name | Machine learning |
| keywords[4].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[4].score | 0.41305097937583923 |
| keywords[4].display_name | Artificial intelligence |
| keywords[5].id | https://openalex.org/keywords/data-mining |
| keywords[5].score | 0.33082032203674316 |
| keywords[5].display_name | Data mining |
| language | en |
| locations[0].id | doi:10.20944/preprints202412.2108.v1 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S6309402219 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Preprints.org |
| locations[0].source.host_organization | |
| locations[0].source.host_organization_name | |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310310987 |
| locations[0].source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.20944/preprints202412.2108.v1 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5080109390 |
| authorships[0].author.orcid | https://orcid.org/0000-0003-0144-575X |
| authorships[0].author.display_name | Kareem Midlej |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Kareem A. Midlej |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5000242504 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3530-1420 |
| authorships[1].author.display_name | Osayd Zohud |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Osayd A. Zohud |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5019563762 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-5292-6778 |
| authorships[2].author.display_name | Iqbal M. Lone |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Iqbal A. Lone |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5023348779 |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Obaida Awadi |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Obaida Awadi |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5078859085 |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Samir Masarwa |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Samir Masarwa |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | https://openalex.org/A5060661565 |
| authorships[5].author.orcid | https://orcid.org/0000-0001-8207-5257 |
| authorships[5].author.display_name | Eva Paddenberg |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Eva Paddenberg-Schubert |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | https://openalex.org/A5019956555 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-2256-2085 |
| authorships[6].author.display_name | Sebastian Krohn |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Sebastian Krohn |
| authorships[6].is_corresponding | False |
| authorships[7].author.id | https://openalex.org/A5044177807 |
| authorships[7].author.orcid | https://orcid.org/0000-0001-9473-8724 |
| authorships[7].author.display_name | Christian Kirschneck |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Christian Kirschneck |
| authorships[7].is_corresponding | False |
| authorships[8].author.id | https://openalex.org/A5058995816 |
| authorships[8].author.orcid | https://orcid.org/0000-0003-1137-5361 |
| authorships[8].author.display_name | Peter Proff |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Peter Proff |
| authorships[8].is_corresponding | False |
| authorships[9].author.id | https://openalex.org/A5065827393 |
| authorships[9].author.orcid | https://orcid.org/0000-0003-1385-7129 |
| authorships[9].author.display_name | Nezar Watted |
| authorships[9].author_position | middle |
| authorships[9].raw_author_name | Nezar Watted |
| authorships[9].is_corresponding | False |
| authorships[10].author.id | https://openalex.org/A5055337126 |
| authorships[10].author.orcid | https://orcid.org/0000-0001-5525-206X |
| authorships[10].author.display_name | Fuad A. Iraqi |
| authorships[10].author_position | last |
| authorships[10].raw_author_name | Fuad A. Iraqi |
| authorships[10].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://doi.org/10.20944/preprints202412.2108.v1 |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Clustering and Machine Learning Models of Skeletal Class I and II Parameters of Arab Orthodontic Patients |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T11363 |
| primary_topic.field.id | https://openalex.org/fields/35 |
| primary_topic.field.display_name | Dentistry |
| primary_topic.score | 0.9412000179290771 |
| primary_topic.domain.id | https://openalex.org/domains/4 |
| primary_topic.domain.display_name | Health Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/3504 |
| primary_topic.subfield.display_name | Oral Surgery |
| primary_topic.display_name | Dental Radiography and Imaging |
| related_works | https://openalex.org/W2961085424, https://openalex.org/W4306674287, https://openalex.org/W4387369504, https://openalex.org/W3046775127, https://openalex.org/W4394896187, https://openalex.org/W3170094116, https://openalex.org/W4386462264, https://openalex.org/W3107602296, https://openalex.org/W4364306694, https://openalex.org/W4312192474 |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | doi:10.20944/preprints202412.2108.v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S6309402219 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Preprints.org |
| best_oa_location.source.host_organization | |
| best_oa_location.source.host_organization_name | |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| best_oa_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.20944/preprints202412.2108.v1 |
| primary_location.id | doi:10.20944/preprints202412.2108.v1 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S6309402219 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Preprints.org |
| primary_location.source.host_organization | |
| primary_location.source.host_organization_name | |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310310987 |
| primary_location.source.host_organization_lineage_names | Multidisciplinary Digital Publishing Institute |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.20944/preprints202412.2108.v1 |
| publication_date | 2024-12-24 |
| publication_year | 2024 |
| referenced_works_count | 0 |
| abstract_inverted_index.I | 16 |
| abstract_inverted_index.II | 22 |
| abstract_inverted_index.In | 230 |
| abstract_inverted_index.ML | 94, 156, 233 |
| abstract_inverted_index.We | 30 |
| abstract_inverted_index.an | 93, 167, 191 |
| abstract_inverted_index.as | 79, 202 |
| abstract_inverted_index.be | 215 |
| abstract_inverted_index.by | 98 |
| abstract_inverted_index.in | 28, 120, 134, 171, 188 |
| abstract_inverted_index.is | 7, 53 |
| abstract_inverted_index.of | 4, 68, 72, 86, 102, 123, 169, 193 |
| abstract_inverted_index.on | 55 |
| abstract_inverted_index.or | 204 |
| abstract_inverted_index.to | 8, 34, 46, 82, 162, 199 |
| abstract_inverted_index.394 | 73 |
| abstract_inverted_index.ANB | 85, 137, 182 |
| abstract_inverted_index.ML, | 190 |
| abstract_inverted_index.The | 1, 65, 106, 117, 154, 207 |
| abstract_inverted_index.age | 148 |
| abstract_inverted_index.aim | 3 |
| abstract_inverted_index.all | 160 |
| abstract_inverted_index.and | 19, 41, 88, 125, 140, 175, 178, 184, 219, 227 |
| abstract_inverted_index.can | 214, 223, 235 |
| abstract_inverted_index.for | 13, 113 |
| abstract_inverted_index.new | 10 |
| abstract_inverted_index.the | 57, 62, 69, 83, 100, 121, 126, 135, 151, 164, 172, 176, 189, 225, 232 |
| abstract_inverted_index.was | 96, 197 |
| abstract_inverted_index.who | 76 |
| abstract_inverted_index.(ML) | 44 |
| abstract_inverted_index.0.78 | 194 |
| abstract_inverted_index.0.87 | 170 |
| abstract_inverted_index.Arab | 26, 74 |
| abstract_inverted_index.SCIO | 203, 218 |
| abstract_inverted_index.This | 51 |
| abstract_inverted_index.Wits | 185 |
| abstract_inverted_index.XXXX | 63 |
| abstract_inverted_index.each | 114 |
| abstract_inverted_index.from | 61 |
| abstract_inverted_index.main | 2 |
| abstract_inverted_index.only | 187 |
| abstract_inverted_index.that | 158, 213 |
| abstract_inverted_index.this | 5 |
| abstract_inverted_index.were | 77 |
| abstract_inverted_index.After | 90 |
| abstract_inverted_index.Tree. | 180 |
| abstract_inverted_index.Using | 181 |
| abstract_inverted_index.Witt. | 89 |
| abstract_inverted_index.among | 25 |
| abstract_inverted_index.angle | 183 |
| abstract_inverted_index.based | 54 |
| abstract_inverted_index.class | 15, 21 |
| abstract_inverted_index.coded | 70 |
| abstract_inverted_index.model | 95, 157, 234 |
| abstract_inverted_index.plan. | 229 |
| abstract_inverted_index.study | 6, 52, 66 |
| abstract_inverted_index.three | 110 |
| abstract_inverted_index.which | 222, 240 |
| abstract_inverted_index.(SCIO) | 18 |
| abstract_inverted_index.SCIIMO | 144, 220 |
| abstract_inverted_index.affect | 224 |
| abstract_inverted_index.angle, | 138 |
| abstract_inverted_index.angle. | 142 |
| abstract_inverted_index.class. | 116 |
| abstract_inverted_index.degree | 122 |
| abstract_inverted_index.detect | 35 |
| abstract_inverted_index.facial | 128 |
| abstract_inverted_index.forest | 174 |
| abstract_inverted_index.gonial | 141 |
| abstract_inverted_index.growth | 129 |
| abstract_inverted_index.models | 45 |
| abstract_inverted_index.random | 173 |
| abstract_inverted_index.should | 241 |
| abstract_inverted_index.showed | 166 |
| abstract_inverted_index.trends | 37 |
| abstract_inverted_index.within | 38, 217 |
| abstract_inverted_index.Israel. | 29 |
| abstract_inverted_index.SCIIMO. | 205 |
| abstract_inverted_index.applied | 42 |
| abstract_inverted_index.between | 150 |
| abstract_inverted_index.center. | 64 |
| abstract_inverted_index.classes | 40 |
| abstract_inverted_index.general | 155 |
| abstract_inverted_index.improve | 242 |
| abstract_inverted_index.lateral | 58 |
| abstract_inverted_index.methods | 12 |
| abstract_inverted_index.models. | 104 |
| abstract_inverted_index.precise | 243 |
| abstract_inverted_index.predict | 47 |
| abstract_inverted_index.present | 216 |
| abstract_inverted_index.records | 71 |
| abstract_inverted_index.(SCIIMO) | 24 |
| abstract_inverted_index.Accuracy | 192 |
| abstract_inverted_index.Besides, | 143 |
| abstract_inverted_index.Methods: | 50 |
| abstract_inverted_index.Results: | 105 |
| abstract_inverted_index.accuracy | 168 |
| abstract_inverted_index.achieved | 198 |
| abstract_inverted_index.analysis | 108, 209 |
| abstract_inverted_index.classify | 163, 200 |
| abstract_inverted_index.clusters | 112, 118, 145 |
| abstract_inverted_index.critical | 36 |
| abstract_inverted_index.diagnose | 237 |
| abstract_inverted_index.distinct | 111 |
| abstract_inverted_index.included | 159 |
| abstract_inverted_index.patients | 27, 75, 165, 201 |
| abstract_inverted_index.pattern, | 130 |
| abstract_inverted_index.patterns | 212 |
| abstract_inverted_index.revealed | 146, 210 |
| abstract_inverted_index.skeletal | 14, 20, 115 |
| abstract_inverted_index.variated | 119 |
| abstract_inverted_index.vertical | 127 |
| abstract_inverted_index.according | 81 |
| abstract_inverted_index.addition, | 231 |
| abstract_inverted_index.analysis, | 92 |
| abstract_inverted_index.appraisal | 186 |
| abstract_inverted_index.assessing | 56 |
| abstract_inverted_index.clusters. | 153 |
| abstract_inverted_index.conducted | 31 |
| abstract_inverted_index.consisted | 67 |
| abstract_inverted_index.diagnosed | 78 |
| abstract_inverted_index.diagnosis | 226 |
| abstract_inverted_index.different | 103, 152 |
| abstract_inverted_index.introduce | 9 |
| abstract_inverted_index.occlusion | 17 |
| abstract_inverted_index.outcomes. | 49 |
| abstract_inverted_index.patients, | 221, 239 |
| abstract_inverted_index.treatment | 228 |
| abstract_inverted_index.Regression | 179 |
| abstract_inverted_index.accurately | 236 |
| abstract_inverted_index.clustering | 33, 91, 107, 208 |
| abstract_inverted_index.evaluating | 99 |
| abstract_inverted_index.identified | 109 |
| abstract_inverted_index.parameters | 60, 136, 161 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Conclusion: | 206 |
| abstract_inverted_index.SCIO/SCIIMO | 238 |
| abstract_inverted_index.differences | 133, 149 |
| abstract_inverted_index.established | 97 |
| abstract_inverted_index.performance | 101 |
| abstract_inverted_index.significant | 132 |
| abstract_inverted_index.substantial | 147 |
| abstract_inverted_index.Panagiotidis | 87 |
| abstract_inverted_index.SCIO/SCIIMO, | 80 |
| abstract_inverted_index.diagnostics. | 244 |
| abstract_inverted_index.hierarchical | 32 |
| abstract_inverted_index.malocclusion | 23, 39 |
| abstract_inverted_index.representing | 131 |
| abstract_inverted_index.cephalometric | 59 |
| abstract_inverted_index.distinguished | 211 |
| abstract_inverted_index.retrognathism | 124 |
| abstract_inverted_index.Classification | 177 |
| abstract_inverted_index.classification | 11, 48 |
| abstract_inverted_index.individualized | 84 |
| abstract_inverted_index.Calculated_ANB, | 139 |
| abstract_inverted_index.machine-learning | 43 |
| abstract_inverted_index.Specificity=0.76) | 196 |
| abstract_inverted_index.(Sensitivity=0.80, | 195 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 11 |
| citation_normalized_percentile.value | 0.42134367 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |