{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T16:42:08Z","timestamp":1791477728845,"version":"4.3.4"},"reference-count":29,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T00:00:00Z","timestamp":1658707200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University","award":["RG-21-09-20"],"award-info":[{"award-number":["RG-21-09-20"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student\u2019s t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LGBM) are used to build classification models to stratify benign-vs.-malignant ovarian cancer patients. Both of the analysis techniques recognized that the serumsamples carbohydrate antigen 125, carbohydrate antigen 19-9, carcinoembryonic antigen and human epididymis protein 4 are the top-most significant biomarkers as well as neutrophil ratio, thrombocytocrit, hematocrit blood samples, alanine aminotransferase, calcium, indirect bilirubin, uric acid, natriumas as general chemistry tests. Moreover, the results from predictive analysis suggest that the machine learning models can classify malignant patients from benign patients with accuracy as good as 91%. Since generally, early-stage detection is not available, machine learning detection could play a significant role in cancer diagnosis.<\/jats:p>","DOI":"10.3390\/jpm12081211","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T21:20:59Z","timestamp":1658784059000},"page":"1211","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":73,"title":["Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1640-6649","authenticated-orcid":false,"given":"Md. Martuza","family":"Ahamad","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8866-743X","authenticated-orcid":false,"given":"Sakifa","family":"Aktar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8483-7059","authenticated-orcid":false,"given":"Md. Jamal","family":"Uddin","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3196-8172","authenticated-orcid":false,"given":"Tasnia","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6200, Bangladesh"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5507-9399","authenticated-orcid":false,"given":"Salem A.","family":"Alyami","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0483-7911","authenticated-orcid":false,"given":"Samer","family":"Al-Ashhab","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5144-7169","authenticated-orcid":false,"given":"Hanan Fawaz","family":"Akhdar","sequence":"additional","affiliation":[{"name":"Department of Physics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5251-2214","authenticated-orcid":false,"given":"AKM","family":"Azad","sequence":"additional","affiliation":[{"name":"Faculty of Science, Engineering & Technology, Swinburne University of Technology, Sydney 2150, Australia"},{"name":"ProCan\u00ae, Children\u2019s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0756-1006","authenticated-orcid":false,"given":"Mohammad Ali","family":"Moni","sequence":"additional","affiliation":[{"name":"School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"284","DOI":"10.3322\/caac.21456","article-title":"Ovarian cancer statistics, 2018","volume":"68","author":"Torre","year":"2018","journal-title":"CA Cancer J. Clin."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1586\/era.09.167","article-title":"First-line treatment of advanced ovarian cancer: Current research and perspectives","volume":"10","author":"Marchetti","year":"2010","journal-title":"Expert Rev. Anticancer Ther."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6127","DOI":"10.1007\/s13277-014-1811-6","article-title":"Diagnostic accuracy of serum HE4, CA125 and ROMA in patients with ovarian cancer: A meta-analysis","volume":"35","author":"Wang","year":"2014","journal-title":"Tumor Biol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"104195","DOI":"10.1016\/j.ijmedinf.2020.104195","article-title":"Using machine learning to predict ovarian cancer","volume":"141","author":"Lu","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"228.e221","DOI":"10.1016\/j.ajog.2010.03.043","article-title":"Comparison of a novel multiple marker assay vs the Risk of Malignancy Index for the prediction of epithelial ovarian cancer in patients with a pelvic mass","volume":"203","author":"Moore","year":"2010","journal-title":"Am. J. Obstet. Gynecol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"437","DOI":"10.6061\/clinics\/2012(05)06","article-title":"A comparison of CA125, HE4, risk ovarian malignancy algorithm (ROMA), and risk malignancy index (RMI) for the classification of ovarian masses","volume":"67","author":"Anton","year":"2012","journal-title":"Clinics"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1158\/1055-9965.98.14.1","article-title":"Endogenous hormones and ovarian cancer: Epidemiology and current hypotheses","volume":"14","author":"Lukanova","year":"2005","journal-title":"Cancer Epidemiol. Biomarkers Prev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1097\/JCE.0000000000000359","article-title":"Ovarian cancer classification using serum proteomic profiling and wavelet features a comparison of machine learning and features selection algorithms","volume":"44","author":"Alqudah","year":"2019","journal-title":"J. Clin. Eng."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3006","DOI":"10.1158\/1078-0432.CCR-18-3378","article-title":"Application of artificial intelligence FOR Preoperative diagnostic And PROGNOSTIC prediction in Epithelial ovarian cancer based on BLOOD BIOMARKERS","volume":"25","author":"Kawakami","year":"2019","journal-title":"Clin. Cancer Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"e65","DOI":"10.3802\/jgo.2019.30.e65","article-title":"Prediction of survival outcomes in patients with epithelial ovarian cancer using machine learning methods","volume":"30","author":"Paik","year":"2019","journal-title":"J. Gynecol. Oncol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4795","DOI":"10.21873\/anticanres.14482","article-title":"Artificial Intelligence in Ovarian Cancer Diagnosis","volume":"40","author":"Akazawa","year":"2020","journal-title":"Anticancer Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"96","DOI":"10.4103\/0972-0707.124171","article-title":"Normal distribution","volume":"17","author":"Krithikadatta","year":"2014","journal-title":"J. Conserv. Dent."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"540","DOI":"10.4097\/kjae.2015.68.6.540","article-title":"T-test as a parametric statistic","volume":"68","author":"Kim","year":"2015","journal-title":"Korean J. Anesthesiol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"22824","DOI":"10.1109\/ACCESS.2021.3056592","article-title":"Comparative Study of Technology With Student\u2019s Perceptions in Indian and Hungarian Universities for Real-Time: Preliminary Results","volume":"9","author":"Verma","year":"2021","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"14516","DOI":"10.1073\/pnas.1810420116","article-title":"A modern maximum-likelihood theory for high-dimensional logistic regression","volume":"116","author":"Sur","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"108288","DOI":"10.1016\/j.measurement.2020.108288","article-title":"A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic","volume":"167","author":"Loey","year":"2021","journal-title":"Measurement"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"10263","DOI":"10.1109\/ACCESS.2021.3050852","article-title":"A Novel Bayesian Optimization-Based Machine Learning Framework for COVID-19 Detection From Inpatient Facility Data","volume":"9","author":"Awal","year":"2021","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"110210","DOI":"10.1016\/j.chaos.2020.110210","article-title":"Spatio-temporal estimation of the daily cases of COVID-19 in worldwide using random forest machine learning algorithm","volume":"140","year":"2020","journal-title":"Chaos Solitons Fractals"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101968","DOI":"10.1016\/j.cose.2020.101968","article-title":"A novel approach for detecting vulnerable IoT devices connected behind a home NAT","volume":"97","author":"Meidan","year":"2020","journal-title":"Comput. Secur."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"85454","DOI":"10.1109\/ACCESS.2020.2991067","article-title":"A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid","volume":"8","author":"Alazab","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"113661","DOI":"10.1016\/j.eswa.2020.113661","article-title":"A machine learning model to identify early stage symptoms of sars-cov-2 infected patients","volume":"160","author":"Ahamad","year":"2020","journal-title":"Expert Syst. Appl."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105161","DOI":"10.1016\/j.compag.2019.105161","article-title":"A method for mining combined data from in-hive sensors, weather and apiary inspections to forecast the health status of honey bee colonies","volume":"169","author":"Braga","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"166509","DOI":"10.1109\/ACCESS.2019.2952609","article-title":"Machine Learning-Based Models for Early Stage Detection of Autism Spectrum Disorders","volume":"7","author":"Akter","year":"2019","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Uddin, S., Khan, A., Hossain, M.E., and Moni, M.A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Med. Inform. Decis. Making, 19.","DOI":"10.1186\/s12911-019-1004-8"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine Learning: Algorithms, Real-World Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_27","first-page":"15","article-title":"Ovarian cancer screening and early detection in the general population","volume":"4","author":"Krivak","year":"2011","journal-title":"Rev. Obstet. Gynecol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gorski, J.W., Quattrone, M., Van Nagell, J.R., and Pavlik, E.J. (2020). Assessing the costs of screening for ovarian cancer in the United states: An evolving analysis. Diagnostics, 10.","DOI":"10.3390\/diagnostics10020067"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-M\u00e1s, J., Bueno-Crespo, A., Khazendar, S., Remezal-Solano, M., Mart\u00ednez-Cend\u00e1n, J.P., Jassim, S., Al Assam, H., Bourne, T., and Timmerman, D. (2019). Evaluation of machine learning methods with Fourier Transform features for CLASSIFYING OVARIAN tumors based on ultrasound images. 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