Introduction: cervical cancer is ranked as the third most prevalent cancer to affect women over the world and a Pap smear seems to be the single most critical intervention to prevent cervical cancer.
Aim: In the present study, the effects of demographic factors (age, education level, job, income level, marriage age, pregnancy, child number, breastfeeding, and menopause), insurance type, disease history (sonography and mammography, breast problem, and cancer) and cancer information on Pap smear screening behavior stage of change were investigated and modeled using an artificial neural network model (ANN).
Materials and Methods: Data were collected from a descriptive-analytical cross-sectional study. This research was conducted on 1898 female employees of governmental agencies of Birjand, a city located in the east of Iran. The questionnaire consisted of four parts (socioeconomic, reproductive characteristics, information about cervical cancer screening, and stage of change for cervical cancer screening). Multilayer feed-forward back-propagation neural networks were used to detect the patterns between variables using a neural network with 14 inputs and one output. To find out the neural network with the minimum sum of squared errors, we evaluated the performance of all neural networks using varying algorithms and numbers of neurons in the hidden layer. For this purpose, the data collected from 1898 women were analyzed using SPSS-22 software.
Results: In the optimal neural network model, the variables of marriage age, age, breastfeeding, and child number were identified as the most significant factors with 18.3, 16.3, 7.3, and 7.3 percentages, respectively, whereas the history of cancer, job, pregnancy, and menopause had importance lower than 5 percentage.
Conclusion: Our findings showed that among many associated variables, the marriage age, age, breastfeeding, and children number were important predictors for the behavioral stage of change. Thus, it seems to focus on these identified factors may lead to the adoption of effective programs and policies to improve cervical cancer screening practice behaviors in women.
Allahyari, Elaheh; Moodi, Mitra; and Tahergorabi, Zoya
"Analysis of Pap Smear Screening Factors using Artificial Neural Network (ANNs),"
BioMedicine: Vol. 12
, Article 2.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.