Improving Disease Diagnosis Through Medical Data Mining and Predictive Analysis: Towards Data-Driven Healthcare
Abstract
The combination of medical data mining and predictive analysis offered a solution to complete the change of the diagnostic paradigm in disease diagnosis. This work focuses on novel approaches for enhancing diagnostic reliability using large, complex data sources, such as EHRs, genomic data, and patient monitoring data. Therefore, through machine learning algorithms, statistical models, as well as pattern recognition, this work will attempt at reap fine-grained cues that could not be discovered by traditional assessment models. Such contributions include the creation of meaningful and efficient models of disease diagnosis and risk prediction. These models adapt a broad spectrum of patients details hence facilitating diagnosis and suggestions for treatment. This research also points to the possibility of the use of AI technology to complement medical data mining in order to improve the accuracy of diagnosis and design efficient clinical processes. To deal with inherent difficulties, including data confidentiality, the lack of common practices, and understanding of algorithms, solutions to make implementation safe and appropriate are provided for the healthcare environment. The study evidence proves the possibilities of the use of data analysis for the effective detection of inaccuracy, the further enhancement of patient's condition, and the organization of the preventive healthcare system. This study therefore emphasizes the importance of embracing new technologies for goal directed and timely, accurate and efficient disease diagnosis for improvement of health care worldwide.