Evaluation and Prediction of the Impact of Parasite Waves and Cell Phone Use by Pregnant Mothers on the Volume of Amniotic Fluid based on Data Mining Algorithms
Introduction: Nowadays, the effects of radiation and constant use of cell phones have led to some problems. These radiations cause disorders in different systems of human body and even in a growing fetus. The aim of this study was to find the effect of using cell phone and internet by pregnant women...
| Main Authors: | , , |
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| Format: | Article |
| Language: | fas |
| Published: |
Kerman University of Medical Sciences
2019-12-01
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| Series: | مجله انفورماتیک سلامت و زیست پزشکی |
| Subjects: | |
| Online Access: | http://jhbmi.ir/article-1-355-en.html |
| Summary: | Introduction: Nowadays, the effects of radiation and constant use of cell phones have led to some problems. These radiations cause disorders in different systems of human body and even in a growing fetus. The aim of this study was to find the effect of using cell phone and internet by pregnant women on the amount of amniotic fluid.
Method: First, a questionnaire was designed and evaluated by obstetricians and telecommunication specialists. The questionnaire includes questions regarding the amount of using cell phone, internet and Wi-Fi during a day. After data collection and preprocessing with predictive Apriori algorithm, the effective factors on the amount of amniotic fluid were investigated and proper associative rules were extracted. The next phase is dedicated to predicting the risks of decreased amniotic fluid by classification algorithms.
Results: Early awareness of doctors as well as the patient can cause avoiding this problem through some related life style changes by mothers. SVM algorithm was successful in arriving at the best results with 80% accuracy.
Conclusion: The most important rule showed that more than 6 hours cell phone conversation, more than 4 hours internet use and more than 8 hours Wi-Fi use by pregnant women can definitely decrease the amount of amniotic fluid. Also, the proposed method, using different classification algorithms, can predict the risk of decreasing the amount of amniotic fluid in pregnant mothers. |
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| ISSN: | 2423-3870 2423-3498 |