ABSTRACT
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
ABSTRACT
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.
ABSTRACT
Background: In both waves of COVID-19 infections, loss of taste was noted in a disproportionately high number of individuals. However, there is a considerable risk of dental disease during and after COVID -19 infections. Aim: Our aim here is to study the oral manifestation of the COVID -19 infections and make a comparison of the severity of presentation in the second wave with the first wave among the general population in Bhubaneswar, city of Odisha, India. Methods: A detailed online questionnaire was developed focusing on the oral manifestation during both the waves using Google forms. Results: Out of a total of 380 RT PCR positive cases, 91/169 and 167/211 cases with oral manifestation were obtained in the first and second waves, respectively. We found 41 (24.26 %) in the first wave and 63 (29.85 %) in the second wave of patients with oral manifestations over the age of 50. Patients receiving oxygen or using a ventilator were found to be 15 (8.9 %) in the first wave and 59 (28%) in the second wave. Conclusion: This is the first study to evaluate the correlation of oral infection with COVID 19 in different waves. This difference could be correlated with the virulence of viruses with mutated strains.
ABSTRACT
In today抯 scenarios many healthcare decisions are being taken by predictive modeling and machine learning techniques. With this review, we focused on logistic regression model, a kind of predictive modeling used in machine learning, and how healthcare researchers take decisions by the help of predictive modeling. For a better data analysis in healthcare, we need to understand the concept of logistic regression as well as others terms, which are linked with it. so that we can clearly understand the concept behind it and implement in medical research. In this review we worked on an example and illustrated how to perform logistic regression using R programming language. The aim of this paper is to understand logistic regression in healthcare and implement it for decision making.