Hybrid Model for Detection of Cervical Cancer Using Causal Analysis and Machine Learning Techniques

Table 3

Comparison of a research review based on machine learning methods.

Article	Technique utilizes	Type of cancer	Important feature discussed	Dataset used	Validation technique
[47]	Artificial neural network	Cancer in breast	Age and mammography results	Diagnostics data and pathological data	Crossvalidation 10-fold
[48]	Support vector machine	Cancer multiple myeloma	STAT1, BRCA1, and CCND1 CCNB1	Online UCI	Crossvalidation 20-fold validation
[49]	Random forest	Cervical cancer	Diet, eating habits, and BME	Clinical data	Crossvalidation 10-fold
[50]	BN methods	Lung cancer	BP, age, and other parameters	Kaggle online dataset	10-fold crossvalidation
[51]	SVM	Cervical cancer, breast cancer	Skin type, breast size, and skin color	Dataset from the hospital (China)	Clinical survey data
[52]	Boruta	Cervical cancer, lung and breast	Age, infection type	Clinical survey data	Crossvalidation
[53]	SVM with random forest	Cervical cancer, cancer in lungs	BME	UCI online dataset	10-fold crossvalidation
[54]	K-NN, SVM	Cervical cancer	Age and mammography results	UCI dataset	Crossvalidation 10-fold