MACHINE LEARNING IMPLEMENTATION IN LUNG CANCER PREDICTION - A SYSTEMATIC LITERATURE REVIEW

被引：0

作者：

Oentoro, Janice ^{[1
]}

Prahastya, Rafif ^{[1
]}

Pratama, Rendy ^{[1
]}

Meiliana ^{[1
]}

Fajar, Muhamad ^{[1
]}

机构：

[1] Bina Nusantara Univ, Sch Comp Sci, Jakarta, Indonesia

来源：

2023 INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE IN INFORMATION AND COMMUNICATION, ICAIIC | 2023年

关键词：

Lung; Cancer Prediction; Machine Learning; Deep Learning; Systematic Review;

D O I：

10.1109/ICAIIC57133.2023.10067128

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Lung cancer is one of the most challenging cancer types to detect amongst all other cancer. Usage of machine learning models can greatly benefit the medical field in predicting lung cancer. Furthermore, numerous datasets are available for training prediction models. The goals of this systematic review are to discover which model is best used in lung cancer prediction and what datasets are used. In conclusion, 70% of the chosen publications utilizes secondary online datasets such as TCGA and TCIA. The machine learning model with the highest accuracy among half of the studies has proven to be SVM with secondary datasets usage.

引用

页码：435 / 439

页数：5

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