A Bayesian Logistic Regression approach in Asthma Persistence Prediction

Background: A number of models based on clinical parameters have been used for the prediction of asthma persistence in children. The number and significance of factors that are used in a proposed model play a cardinal role in prediction accuracy. Different models may lead to different significant variables. In addition, the accuracy of a model in medicine is really important since an accurate prediction of illness persistence may improve prevention and treatment intervention for the children at risk. Methods: Data from 147 asthmatic children were analyzed by a new method for predicting asthma outcome using Principal Component Analysis (PCA) in combination with a Bayesian logistic regression approach implemented by the Markov Chain Monte Carlo (MCMC). The use of PCA is required due to multicollinearity among the explanatory variables. Results: This method using the most appropriate models seems to predict asthma with an accuracy of 84.076% and 86.3673%, a Sensitivity of 84.96% and 87.25% and a Specificity of 83.22% and 85.52%, respectively. Conclusion: Our approach predicts asthma with high accuracy, gives steadier results in terms of positive and negative patients and provides better information about the influence of each factor (demographic, symptoms etc.) in asthma prediction

Fever status detection using artificial neuron network

This research paper proposes a monitoring system and a prototype that has been developed for detecting if a when fever is present in senior citizens or any other specific groups of people requiring continuous care. With various issues affecting the health of senior citizens, it is imperative to continuously monitor their health status. The monitoring system is beneficial as it will make it feasible to enable the real time detection of fever and thus allowing for the early treatment. Delaying treatment can lead to the underlining health issue going beyond the remediable condition. Thus, quick detection is vital. There are various issues that might causes illness in people. Some of the issues include virus outbreak, seasonal infections, disease, and old age. In this paper our focus is mainly on old age. This group of people is much more at risk of getting ill or frequently need more attention. In this project, the presence of fever or illness has been detected by using artificial intelligence (AI). The AI technique that is utilized in this project is artificial neural networks. The computation is done by first training the system and then secondly validating the trained system. After the training, the system is supplied with a new set of data, with a known state, to validate that the training was successful. To validate the system, it is provided with sample data to test its efficiency. If the system is well trained the validation data would label that data correctly. That label is known before the validation test, as the sample data had known labels. These known labels were not given to training but not validation system. The system is function properly if its label matched the sample data label. The conducted experiment demonstrated a successful detection with an efficiency rate of 82 percent.Faculty of Applied Informatics, Tomas Bata University in Zlin [IGA/CebiaTech/2021/001

Prediction models for childhood asthma: a systematic review

Background The inability to objectively diagnose childhood asthma before age five often results in both under‐treatment and over‐treatment of asthma in preschool children. Prediction tools for estimating a child's risk of developing asthma by school‐age could assist physicians in early asthma care for preschool children. This review aimed to systematically identify and critically appraise studies which either developed novel or updated existing prediction models for predicting school‐age asthma. Methods Three databases (MEDLINE, Embase and Web of Science Core Collection) were searched up to July 2019 to identify studies utilizing information from children ≤5 years of age to predict asthma in school‐age children (6‐13 years). Validation studies were evaluated as a secondary objective. Results Twenty‐four studies describing the development of 26 predictive models published between 2000 and 2019 were identified. Models were either regression‐based (n = 21) or utilized machine learning approaches (n = 5). Nine studies conducted validations of six regression‐based models. Fifteen (out of 21) models required additional clinical tests. Overall model performance, assessed by area under the receiver operating curve (AUC), ranged between 0.66 and 0.87. Models demonstrated moderate ability to either rule in or rule out asthma development, but not both. Where external validation was performed, models demonstrated modest generalizability (AUC range: 0.62‐0.83). Conclusion Existing prediction models demonstrated moderate predictive performance, often with modest generalizability when independently validated. Limitations of traditional methods have shown to impair predictive accuracy and resolution. Exploration of novel methods such as machine learning approaches may address these limitations for future school‐age asthma predictio