A Retrospective: Sightseeing Excursion of Threatened Miscarriage Pertaining Ensemble Machine Learning Algorithms

The manifested invention is associated with a miscarriage/stillbirth prediction using the ensemble learning methodology. The elementary data is congregated from various rural areas of 9 different states in India with 15258 rows and 201 columns. Further, data is processed to acquire useful features such as age, intake of tobacco, alcohol, smoking, diagnosis with any disease, awareness of danger, is currently pregnant, the outcome of the pregnancy and so forth. The outcome signifies that the proposed method has the capability of extracting the chances of miscarriage with preferable accuracy and classifying them in a fruitful way. This research includes the implementation of various machine learning algorithms namely Adaboost, Random forest, bagging, and boosting which are clubbed together by using a voting classifier to obtain accuracy, precision, recall and f1-score based on ensemble methods in order to generate precise results. Raising awareness of the concern which can emerge in pregnancy, and how to spot the symptoms, can help save lives. Therefore, this invention impetus mothers to maintain their health status on the behalf of data prediction set used.