An Integrated Machine Learning Model for Disease Diagnosis in a Healthcare Application

Authors

Rahul Roy Devarakonda

Abstract

Disease diagnosis has been transformed by the use of machine learning (ML) in healthcare, which improves patient outcomes, decreases manual labour, and increases predictive accuracy. This work presents an integrated machine-learning model for disease diagnosis in a medical setting. The proposed model analyzes medical data, forecasts the presence of diseases, and assists healthcare workers in making informed decisions by utilizing supervised learning approaches and deep learning algorithms. Electronic health records (EHRs), medical imaging, and organized clinical data are all integrated into the model, enabling it to identify patterns associated with various illnesses. By addressing key issues, including data heterogeneity, class imbalance, and interpretability, the proposed approach ensures a more trustworthy and comprehensible decision-making process. Experiments have demonstrated that the model outperforms conventional diagnostic techniques, accurately predicting diseases with high sensitivity and specificity. Furthermore, this study examines the role of big data analytics in healthcare, emphasizes the advantages of deep learning in disease diagnosis and medical imaging, and discusses the moral and legal issues surrounding predictive analytics in healthcare. According to the results, ML-based disease diagnosis may improve patient outcomes, increase early detection, and streamline clinical workflows, opening the door for further developments in AI-powered healthcare systems.

Keywords

Healthcare Applications, Disease Diagnosis, Predictive Analytics, Electronic Health Records (EHR), Automated Diagnosis Systems

An Integrated Machine Learning Model for Disease Diagnosis in a Healthcare Application. Rahul Roy Devarakonda. 2016. IJIRCT, Volume 2, Issue 1. Pages 1-8. https://www.ijirct.org/viewPaper.php?paperId=2503071