Screening ovarian cancer by using risk factors: machine learning assists

Table 2 Analysis of OC predictors using MLR

Feature	β	OR	95% CI of OR	P_-value
Age	0.521	1.275	[1.215–1.472]	0.01
BMI	0.334	1.179	[1.079–1.343]	0.01
Blood group	0.188	1.121	[1.053–1.278]	0.03
Race	0.052	0.927	[0.892–1.148]	0.1
Menopausal age	0.294	1.03	[1.012–1.196]	0.04
Postmenopausal hormone therapy	0.255	1.24	[1.191–1.334]	0.01
Endometriosis	0.451	1.645	[1.572–1.837]	< 0.001
History of nonpregnancy	0.674	1.994	[1.727–2.446]	< 0.001
Family history of cancer such as ovary, breast, or colorectal	0.319	1.274	[1.256–1.349]	0.01
Family cancer syndrome	0.118	1.032	[1.011–1.056]	0.045
Fertility treatment use	0.072	0.958	[0.873–1.156]	0.07
Breast cancer	0.174	1.056	[1.023–1.103]	0.04
Smoking	0.293	1.155	[1.093–1.257]	0.03
History of pregnancy and breastfeeding before age 26	0.252	1.089	[1.036–1.157]	0.04
History of PCOS	0.378	1.526	[1.455–1.724]	0.01
History of chest X-ray	0.412	1.256	[1.181-0.1.324]	0.02
Alcohol consumption	0.163	1.163	[0.776–1.554]	0.165
Particular food consumption, such as fried foods, whole milk, and trans fats	0.434	2.016	[1.774–2.347]	< 0.001
History of exposure to mutagenic or chemical substances	0.062	0.974	[0.665–1.257]	0.12
High red meat consumption	0.126	1.072	[0.824–1.123]	0.08
History of hysterectomy	0.538	1.986	[1.795–2.623]	< 0.001
Oral contraceptive pill use	− 0.473	0.512	[0.345–0.679]	< 0.001
Aspirin use	− 0.225	0.498	[0.452–0.667]	0.01
High consumption of coffee	0.16	0.773	[0.572–1.231]	0.13
Vegetable consumption	0.075	0.892	[0.652–1.453]	0.185
Fruit consumption	0.09	0.805	[0.452–1.375]	0.123

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