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A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: A Machine Learning Perspective
Dung Nguyen Tien;
Huong Bui Thii Thu;
Tram Hoang Thi Ngoc;
Thuy Pham Thi;
Trung Nguyen Dac;
Huyen Nguyen Thi Thu;
Hang Vu Thi Thu;
Lan Anh Thi Luong;
Lan Thu Hoang;
Ho Cam Tu;
Nina Koeber;
Tanja Bauer;
Lam Khanh Ho
ABSTRACT
Background:
Background:
Hepatitis B virus (HBV) mother-to-child transmission (MTCT) can occur by transplacental infection or blood-to-blood contact around or after delivery. Data mining techniques are essential in clinical decision-making, especially in medical diagnostics.
Objective:
Objective:
In this study, we aimed to bring the actual data set into the decision tree model and understand the information that will be released.
Methods:
Methods:
We experimented with decision tree calculations by using ID3 and CART algorithms to find general rules and predictions for a data set of 60 HBsAg-positive pregnant women.
Results:
Result: Around 42 to 47% of cases of HBeAg positive fall to the large risk of MTCT. 15 to 18% of cases of HBeAg negative and a concentration of PBMCs ≥8x106 cells/ml will be at a small risk of MTCT. 37 to 53% cases of HBeAg negative and concentration of PBMCs < 8x106 cells/ml will negligent the risk of MTCT.
Conclusions:
Conclusion: Combining experiment results shows the additive or inhibitory influence of other biochemical indicators on the impact of HBeAg and concentration of PBMCs on the risk of transmission from mother to child.
Citation
Please cite as:
Nguyen Tien D, Bui Thii Thu H, Hoang Thi Ngoc T, Pham Thi T, Nguyen Dac T, Nguyen Thi Thu H, Vu Thi Thu H, Thi Luong LA, Thu Hoang L, Cam Tu H, Koeber N, Bauer T, Khanh Ho L
A Data-Driven Approach to Assessing Hepatitis B Mother-to-Child Transmission Risk Prediction Model: Machine Learning Perspective
