The Application of Machine Learning in Cervical Cancer Prediction

Yin, Qihui

doi:10.1145/3468891.3468894

Cited by 4 publications

(2 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Initially they start with passive motion, and then over the time it is updated with active motion. The passive motion is based on its own location and the updated location at time t+1 is given as (14). 1 * (0,1) *( )…”

Section: B the Jsa Conceptsmentioning

confidence: 99%

See 1 more Smart Citation

Cervical Cancer Diagnosis Using Intelligent Living Behavior of Artificial Jellyfish Optimized With Artificial Neural Network

et al. 2022

View full text Add to dashboard Cite

Cervical cancer affects nearly 4% of the women across the globe and leads to mortality if not treated in early stage. A few decades before, the mortality rate was too high when compared to the present statistics. This is achieved as nowadays most of women are aware of this disease and undergo health examination mainly for screening cervical cancer on regular basis. But only the accurate diagnosis can be helpful for further treatment. Many works are carried out for accurate diagnosis and always have some limitations in accurate predictions. In this work, an efficient algorithm is proposed for the accurate diagnosis of cervical cancer. A meta-heuristic called artificial Jellyfish search optimizer (JS) algorithm is combined with artificial neural network (ANN) to tackle this problem. The proposed algorithm is called JellyfishSearch_ANN and is employed to classify the cervical cancer dataset with four type of targets based on the examination. The JellyfishSearch_ANN provides outstanding results among other classifiers taken for comparison and mainly its classification accuracy is found to be above 98.87% for all targets.

show abstract

Section: B the Jsa Conceptsmentioning

confidence: 99%

“…The methods such as Random forest, Decision tree, Logistic regression and Ada boost were applied to the dataset [11], also by including a weighted version [14]. The performance of each model is assessed in terms of both weighted and unweighted version of the dataset.…”

Section: Introductionmentioning

confidence: 99%