The Effectiveness of Machine Learning Systems' Accuracy in Predicting Heart Stroke Using Socio-Demographic and Risk Factors - A Comparative Analysis of Various Models

Background: Cardiologists can more appropriately classify patients' cardiovascular diseases by executing ac-curate diagnoses and prognoses, enabling them to administer the most appropriate care. Due to machine learning's ability to identify patterns in data, its applications in the medical sector have grown. Diagnosticians can avoid making mistakes by classifying the incidence of cardiovascular illness using machine learning. To lower the fatality rate brought on by cardiovascular disorders, our research developed a model that can cor-rectly forecast these conditions.Methods: This study emphasized a model that can correctly forecast cardiovascular illnesses to lower the death rate brought on by these conditions. We deployed four well-known classification machine learning al-gorithms like K nearest Neighbour, Logistic Regression, Artificial Neural network, and Decision tree. Results: The proposed models were evaluated by their performance matrices. However logistic regression performed high accuracy concerning AUC (0.955) 95% CI (0.872-0.965) followed by the artificial neural net-work. AUC (0.864) 95% CI (0.826-0.912). Conclusion: Individuals' risk of having a cardiac event may be predicted using machine learning, and those who are most at risk can be identified. Predictive models may be developed via machine learning to pinpoint those who have a high chance of suffering a heart attack

Comparison Of Artificial Neural Network and Decision Tree Methods for Predicting the Maternal Outcome in A Tertiary Care Hospital in Odisha, India

Background: This study used an artificial neural network (ANN) and a decision tree to predict maternal outcomes and their major determinants. An artificial neural network (ANN) and a decision tree were used in this study to determine maternal outcomes and their significant determinants. Methods: Data was gathered from 955 pregnant women at a tertiary care hospital in Bhubaneswar, Od-isha. A popular machine learning algorithm, artificial neural networks (ANN), was used to predict mater-nal outcomes and their determinants. Results: In the bivariate analysis, we found gestational age is significantly associated with maternal out-come (p=<0.001). The accuracy of the ANN model and decision tree was 0.882 and 0.823, respectively. Based on the variable importance of ANN, the significant determinants of maternal outcome were birth weight, systolic blood pressure, haemoglobin, gestational age, age of mother, diastolic blood pressure etc. Conclusion: This model can be utilized in future for Proper precautions and medical check-ups required during the maternal period to avoid a negative maternal outcome.