Optimizing Cervical Cancer Prediction, Harnessing the Power of Machine Learning for Early Diagnosis

被引：1

作者：

Hasan, Mahadi ^{[1
]}

Islam, Jahirul ^{[2
]}

Al Mamun, Miraz ^{[3
]}

Mim, Afrin Akter ^{[4
]}

Sultana, Sharmin ^{[5
]}

Sabuj, Md Sanowar Hossain ^{[6
]}

机构：

[1] Emory Healthcare, Chattanooga, TN 37421 USA

[2] New Mexico Inst Min & Technol, Dept Comp Sci & Engn, Socorro, NM 87801 USA

[3] CHRISTUS Hlth, Dallas, TX USA

[4] Daffodil Int Univ, Dept Comp Sci & Engn, Dhaka, Bangladesh

[5] New Mexico Inst Min & Technol, Dept Math, Socorro, NM 87801 USA

[6] Texas A&M Univ Commerce, Dept Business Analyt, Commerce, TX USA

来源：

2024 IEEE 5TH ANNUAL WORLD AI IOT CONGRESS, AIIOT 2024 | 2024年

关键词：

Cervical Cancer; Machine Learning; Random Forest; AdaBoost; SVM;

D O I：

10.1109/AIIoT61789.2024.10578997

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Cervical cancer is one of the most widespread ovarian cancers in the world. It is linked up with multiple risk factors such as Sexually transmitted diseases, human papillomavirus and smoking. Death rate can be reduced if early diagnosis is possible. In addition if early prediction can be possible it will help greatly patients as well as doctors to give them proper treatment immodestly. Our study focuses on various machine learning algorithms to forecast early detection of cervical cancer. Dataset for this work has been collected from kaggle.com. The given dataset consists of various demographic and medical features related to an individual's sexual and reproductive health. With proper tuning of parameters using cross-validation in the training set, the XGB Classifier achieves an accuracy of 98% and a ROC AUC of 99%.

引用

页码：0552 / 0556

页数：5

共 50 条

[31] Machine learning and multiparametric MRI for early diagnosis of prostate cancer
Bonekamp, D.
Schlemmer, H. -P.
UROLOGE, 2021, 60 (05): : 576 - 591
[32] Prediction of Cervical Cancer from Behavior Risk Using Machine Learning Techniques
Akter L.
Ferdib-Al-Islam
Islam M.M.
Al-Rakhami M.S.
Haque M.R.
SN Computer Science, 2021, 2 (3)
[33] A Machine Learning-Based Framework for the Prediction of Cervical Cancer Risk in Women
Kaushik, Keshav
Bhardwaj, Akashdeep
Bharany, Salil
Alsharabi, Naif
Rehman, Ateeq Ur
Eldin, Elsayed Tag
Ghamry, Nivin A.
SUSTAINABILITY, 2022, 14 (19)
[34] Using Machine Learning algorithms for breast cancer risk prediction and diagnosis
Bharat, Anusha
Pooja, N.
Reddy, R. Anishka
2018 3RD INTERNATIONAL CONFERENCE ON CIRCUITS, CONTROL, COMMUNICATION AND COMPUTING (I4C), 2018,
[35] Role of Artificial Intelligence and Machine Learning in Prediction, Diagnosis, and Prognosis of Cancer
Gaur, Kritika
Jagtap, Miheer M.
CUREUS JOURNAL OF MEDICAL SCIENCE, 2022, 14 (11)
[36] Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis
Asri, Hiba
Mousannif, Hajar
Al Moatassime, Hassan
Noel, Thomas
7TH INTERNATIONAL CONFERENCE ON AMBIENT SYSTEMS, NETWORKS AND TECHNOLOGIES (ANT 2016) / THE 6TH INTERNATIONAL CONFERENCE ON SUSTAINABLE ENERGY INFORMATION TECHNOLOGY (SEIT-2016) / AFFILIATED WORKSHOPS, 2016, 83 : 1064 - 1069
[37] Harnessing the Power of Ensemble Machine Learning for the Heart Stroke Classification
Pal P.
Nandal M.
Dikshit S.
Thusu A.
Singh H.V.
EAI Endorsed Transactions on Pervasive Health and Technology, 2023, 9 (01)
[38] Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology
Raoof Nopour
BMC Gastroenterology, 25 (1)
[39] Harnessing the power of machine learning for crop improvement and sustainable production
Khatibi, Seyed Mahdi Hosseiniyan
Ali, Jauhar
FRONTIERS IN PLANT SCIENCE, 2024, 15
[40] Machine learning prediction of early recurrence after surgery for gallbladder cancer
Catalano, Giovanni
Alaimo, Laura
Chatzipanagiotou, Odysseas P.
Ruzzenente, Andrea
Aucejo, Federico
Marques, Hugo P.
Lam, Vincent
Hugh, Tom
Bhimani, Nazim
Maithel, Shishir K.
Kitago, Minoru
Endo, Itaru
Pawlik, Timothy M.
BRITISH JOURNAL OF SURGERY, 2024, 111 (11)

← 1 2 3 4 5 →