SMART DISEASE DIAGNOSIS USING CNN AND KALMAN FILTERS: INTEGRATING STRUCTURED AND UNSTRUCTURED MEDICAL DATA

Abstract [English]

In the realm of healthcare analytics, the accuracy of disease prediction models often suffers due to the incomplete nature of medical data and the regional variability of disease patterns. Traditional approaches have primarily focused on structured data, thereby neglecting the potential insights hidden in semi-structured and unstructured formats such as medical notes, diagnostic reports, and imaging data. This project introduces a hybrid system that leverages Convolutional Neural Networks (CNNs) for effective feature extraction from unstructured data and Kalman Filters for dynamic tracking and smoothing of patient health states over time.
The proposed system is designed to handle and integrate both structured and unstructured data sources to enhance the predictive accuracy of disease analysis. CNNs are employed to process complex textual and visual inputs, transforming them into structured feature representations. These are subsequently combined with temporal observations in a Kalman Filter framework to predict disease progression and identify potential anomalies in patient profiles.
Our aim is to develop an intelligent support system that aids healthcare professionals and consumers in diagnosing diseases more accurately and selecting treatment plans based on a comprehensive analysis of symptoms, regional trends, and personal health records. By accommodating diverse data types and regional disease characteristics, this system not only improves the reliability of disease outbreak predictions but also personalizes healthcare recommendations. The integration of CNN and Kalman Filter technologies ensures a robust, real-time, and adaptive diagnostic tool suitable for dynamic clinical environments.