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

Table 4

The hypothesis to find the relationship between the parameters.

S. no.	Hypothesis
	Dependent parameter	Possible predictors
1	Sexual partners’ frequency	Sexual partners’ frequency, Dix: cancer, Dix, STDs: hormonal contraception via hormones (years) vulvoperinea_lcondy_lomatosis
2	Dix: cancer	Sexual partners’ frequency, Dix: cancer, Dix, STDs: hormonal contraception via hormones (years) vulvoperinea_lcondy_lomatosis
3	STDs: vulvoperinea_lcondy_lomatosis	Sexual partners’ frequency, Dix: cancer, Dix, STDs: hormonal contraception via hormones (years), vulvoperinea_lcondy_lomatosis
4	STDs: condy_lomatosis	Sexual partners’ frequency, Dix: cancer, Dix, STDs: contraception via hormones (years), vulvoperinea_lcondy_lomatosis
5	Contraception via hormones (years)	Sexual partners’ frequency, Dix: cancer, Dix, STDs: contraception via hormones (years), vulvoperinea_lcondy_lomatosis