首页|Data from Debark University Broaden Understanding of Artificial Intelligence (Ex plainable artificial intelligence models for predicting pregnancy termination am ong reproductive-aged women in six east African countries: machine learning appr oach)
Data from Debark University Broaden Understanding of Artificial Intelligence (Ex plainable artificial intelligence models for predicting pregnancy termination am ong reproductive-aged women in six east African countries: machine learning appr oach)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New study results on artificial intell igence have been published. According to news reporting from Debark University b y NewsRx journalists, research stated, "Pregnancy termination remains a complex and sensitive issue with approximately 45% of abortions worldwide being unsafe, and 97% of abortions occurring in developing countri es. Unsafe pregnancy terminations have implications for women's reproductive hea lth." The news editors obtained a quote from the research from Debark University: "Thi s research aims to compare black box models in their prediction of pregnancy ter mination among reproductive-aged women and identify factors associated with preg nancy termination using explainable artificial intelligence (XAI) methods. We us ed comprehensive secondary data on reproductive-aged women's demographic and soc ioeconomic data from the Demographic Health Survey (DHS) from six countries in E ast Africa in the analysis. This study implemented five black box ML models, Bag ging classifier, Random Forest, Extreme Gradient Boosting (XGB) Classifier, CatB oost Classifier, and Extra Trees Classifier on a dataset with 338,904 instances and 18 features. Additionally, SHAP, Eli5, and LIME XAI techniques were used to determine features associated with pregnancy termination and Statistical analysi s were employed to understand the distribution of pregnancy termination. The res ults demonstrated that machine learning algorithms were able to predict pregnanc y termination on DHS data with an overall accuracy ranging from 79.4 to 85.6% . The ML classifier random forest achieved the highest result, with an accuracy of 85.6%."
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