Despite significant improvement in prognosis, myocardial infarction (MI) remains a major cause of morbidity and mortality around the globe. MI is a life-threatening cardiovascular condition that requires prompt diagnosis and appropriate treatment. The primary objective of this research is to identify instances of anterior and inferior myocardial infarction by utilizing data obtained from Ultra-wideband radar technology in a hospital for patients of anterior and inferior MI. The collected data is preprocessed to extract spectral features. A novel feature engineering approach is designed to fuse temporal features and class prediction probability features derived from the spectral feature dataset. Several well-known machine learning models are implemented and fine-tuned to obtain optimal performance in the detection of anterior and inferior MI. The results demonstrate that integration of the fused feature set with machine learning models results in a notable improvement in both the accuracy and precision of MI detection. Notably, random forest (RF) and k-nearest neighbor showed superb performance with an accuracy of 98.8%. For demonstrating the capacity of models to generalize, K-fold cross-validation is carried out, wherein RF exhibits a mean accuracy of 99.1%. Furthermore, the examination of computational complexity indicates a low computational complexity, thereby indicating computational efficiency.

Cardiovascular disease is common in woman of all age, including child bearing age. In this study, we aim to compare maternal and fetal outcome in pregnant woman with and without preexisting cardiovascular disease. This case control single center study was conducted by Obstetrics & Gynecology department and Cardiology department Shaikh Zayed Medical College/Hospital Rahimyar Khan from 1^st March 2020 to 30^th June 2020. Pregnant woman with preexisting cardiovascular disease had more preterm births and newborn with lower birth weight. Maternal and fetal deaths were numerical higher in pregnant women with preexisting cardiovascular disease but statistically non-significant compared to woman without preexisting cardiovascular disease. It is important to identify underlying cardiovascular disease in pregnant woman. Proper counselling throughout pregnancy is needed and efforts should be made to minimize risk of maternal and fetal complications.

Background: Electrolyte imbalances, particularly in potassium and magnesium, are prevalent in patients with new-onset atrial fibrillation (AF) and may influence in-hospital outcomes. Objective: To evaluate the association between serum potassium and magnesium imbalances and in-hospital morbidity and mortality in patients with new-onset AF. Methods: This prospective observational study included 220 patients admitted with new-onset AF. Serum potassium and magnesium levels were measured upon admission. Clinical outcomes, including in-hospital morbidity and mortality, were recorded. Statistical analyses were performed to assess associations. Results: Of the 220 patients, 45% had hypokalemia (<3.5 mEq/L), and 38% had hypomagnesemia (<1.8 mg/dL). Hypokalemia was associated with increased incidence of arrhythmias (p<0.01) and prolonged hospital stay (p<0.05). Hypomagnesemia correlated with higher mortality rates (p<0.05). Multivariate analysis confirmed that both hypokalemia and hypomagnesemia were independent predictors of adverse in-hospital outcomes. Conclusion: Serum potassium and magnesium imbalances are significant predictors of in-hospital morbidity and mortality in patients with new-onset AF. Monitoring and correction of these electrolytes may improve patient outcomes. Atrial fibrillation, serum potassium, serum magnesium, electrolyte imbalance, in-hospital outcomes, mortality, morbidity.