Abstract
Background: Within modern health systems, the possibility of accessing a large amount and a variety of data
related to patients’ health has increased significantly over the years. The source of this data could be mobile and
wearable electronic systems used in everyday life, and specialized medical devices. In this study we aim to
investigate the use of modern Machine Learning (ML) techniques for preclinical health assessment based on data
collected from questionnaires filled out by patients.
Method: To identify the health conditions of pregnant women, we developed a questionnaire that was distributed
in three maternity hospitals in the Mureș County, Romania. In this work we proposed and developed an ML
model for pattern detection in common risk assessment based on data extracted from questionnaires.
Results: Out of the 1278 women who answered the questionnaire, 381 smoked before pregnancy and only 216
quit smoking during the period in which they became pregnant. The performance of the model indicates the
feasibility of the solution, with an accuracy of 98 % confirmed for the considered case study.
Conclusion: The proposed solution offers a simple and efficient way to digitize questionnaire data and to analyze
the data through a reduced computational effort, both in terms of memory and computing power used.
related to patients’ health has increased significantly over the years. The source of this data could be mobile and
wearable electronic systems used in everyday life, and specialized medical devices. In this study we aim to
investigate the use of modern Machine Learning (ML) techniques for preclinical health assessment based on data
collected from questionnaires filled out by patients.
Method: To identify the health conditions of pregnant women, we developed a questionnaire that was distributed
in three maternity hospitals in the Mureș County, Romania. In this work we proposed and developed an ML
model for pattern detection in common risk assessment based on data extracted from questionnaires.
Results: Out of the 1278 women who answered the questionnaire, 381 smoked before pregnancy and only 216
quit smoking during the period in which they became pregnant. The performance of the model indicates the
feasibility of the solution, with an accuracy of 98 % confirmed for the considered case study.
Conclusion: The proposed solution offers a simple and efficient way to digitize questionnaire data and to analyze
the data through a reduced computational effort, both in terms of memory and computing power used.