Machine Learning approaches serve as well-known and significant in the field of clinical
research. The scope of cardiovascular diseases (CVD) influence the heart and nerves is a
typical study during the human life cycle. Early identification of cardiovascular infection and
procedures prompted the survival chances of patients to decrease vulnerability. The healthcare
industry contains lots of clinical data. AI assessments are needed to make decisions feasibly for
calculating heart ailments. The Predictive Analysis of Fetal Cardiovascular Disease using
SMOTE (Synthetic Minority Oversampling Technique) investigates the relative performance of
various machine learning methods such as Logistic Regression, Naive Bayes, SGD Classifier,
KNN, Decision Tree, Random Forest, Support Vector Machine (SVM), XGBoost Classifier via
medical records. The crucial task is to develop a model with a skewed dataset and produce
accurate results. SMOTE technique addressed the issue of imbalanced data to balance the
positive and negative samples of the dataset. In addition to this, the model expects to predict
the fetus's survival chances if it has any CVD ailment.
This research based project ensures the good health and wellbeing of newborn babies or infants by accurate and timely diagnosis of cardiovascular disease in them. The system is indigenously developed and ensures economic growth and development in the medical equipment industry in Pakistan. Currently RCAI is seeking collaborators whose goal is to develop innovative solutions nationally.