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

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