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.

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.