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Article

Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches

by
Md. Martuza Ahamad
1,
Sakifa Aktar
1,
Md. Jamal Uddin
1,
Tasnia Rahman
2,
Salem A. Alyami
3,
Samer Al-Ashhab
3,
Hanan Fawaz Akhdar
4,
AKM Azad
5,6 and
Mohammad Ali Moni
7,*
1
Department of Computer Science and Engineering, Bangabandhu Sheikh Mujibur Rahman Science and Technology University, Gopalganj 8100, Bangladesh
2
Department of Computer Science and Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6200, Bangladesh
3
Department of Mathematics and Statistics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
4
Department of Physics, Faculty of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
5
Faculty of Science, Engineering & Technology, Swinburne University of Technology, Sydney 2150, Australia
6
ProCan®, Children’s Medical Research Institute, Faculty of Medicine and Health, The University of Sydney, Westmead, NSW 2145, Australia
7
School of Health and Rehabilitation Sciences, Faculty of Health and Behavioural Sciences, The University of Queensland, St Lucia, QLD 4072, Australia
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2022, 12(8), 1211; https://doi.org/10.3390/jpm12081211
Submission received: 25 June 2022 / Revised: 7 July 2022 / Accepted: 21 July 2022 / Published: 25 July 2022
(This article belongs to the Section Mechanisms of Diseases)
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Abstract

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’s 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.
Keywords: ovarian cancer; benign ovarian tumors; tumor marker; machine learning; statistical analysis ovarian cancer; benign ovarian tumors; tumor marker; machine learning; statistical analysis

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MDPI and ACS Style

Ahamad, M.M.; Aktar, S.; Uddin, M.J.; Rahman, T.; Alyami, S.A.; Al-Ashhab, S.; Akhdar, H.F.; Azad, A.; Moni, M.A. Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches. J. Pers. Med. 2022, 12, 1211. https://doi.org/10.3390/jpm12081211

AMA Style

Ahamad MM, Aktar S, Uddin MJ, Rahman T, Alyami SA, Al-Ashhab S, Akhdar HF, Azad A, Moni MA. Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches. Journal of Personalized Medicine. 2022; 12(8):1211. https://doi.org/10.3390/jpm12081211

Chicago/Turabian Style

Ahamad, Md. Martuza, Sakifa Aktar, Md. Jamal Uddin, Tasnia Rahman, Salem A. Alyami, Samer Al-Ashhab, Hanan Fawaz Akhdar, AKM Azad, and Mohammad Ali Moni. 2022. "Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches" Journal of Personalized Medicine 12, no. 8: 1211. https://doi.org/10.3390/jpm12081211

APA Style

Ahamad, M. M., Aktar, S., Uddin, M. J., Rahman, T., Alyami, S. A., Al-Ashhab, S., Akhdar, H. F., Azad, A., & Moni, M. A. (2022). Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches. Journal of Personalized Medicine, 12(8), 1211. https://doi.org/10.3390/jpm12081211

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