Integrated Machine Learning Approaches for Early Infectious Disease Diagnosis
Keywords:
Key feature, biomarkers, machine learning algorithms, healthcare professional, MAXQDAAbstract
In the reality of global health issues, infectious diseases are complex and challenging, requiring innovative approaches for accurate and timely diagnosis. The primary goal of this research is explore a machine learning algorithm and to determine the key features and biomarkers that may be used in the early detection of infectious diseases. The research design for this study was qualitative data. The main data source was gathered through an open-ended questionnaire for thematic analysis. A systematic search was conducted in the references for the use of machine learning to infectious diseases. Healthcare professionals make representation for the Focus Group Discussion (FGD). MAXQDA software was used to examine the FGD data using context analysis. Primary and secondary data collection are also part of the study. Following infection control policies is considered one of the best practices of healthcare facilities to alleviate the impact of infectious diseases. Key features and biomarkers of infectious diseases help the healthcare professional make a diagnosis earlier and prevent severe complications. The identified key features and biomarkers depend on the stage of infection, symptoms, and diagnosis. The findings of this research study could lead to more improvements in public health plans and advancement in the management of infectious diseases. ML algorithms include eXtreme Gradient Boosting (XGBoost), random forest (RF), long short-term memory (LSTM), support vector machine (SVM), and convolutional neutral network (CNN), improves accuracy in diagnosing infectious diseases. Continuing research studies can be used to develop more hybrid prediction models.
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