Advanced Predictive Model for Chronic Disease Diagnosis Employing Deep Learning Approaches

In the current context of health care, digitalization takes advantage of advancements that have been made within clinical services. In the current scenario, the prediction and diagnosis of chronic diseases are quite tricky with traditional methodologies, considering vast amounts of data generated to monitor and analyze the outcomes of patients. The modern technologies use statistical analyses and machine learning to replace the traditional methods. The tools are very effective in extracting meaningful information from heaps of medical data, which gives a more nuanced and clearer picture of the evolving disease and the health of the patient. Applying deep learning algorithms, the new research provides a complete result for diseases based on the symptoms that the patients are experiencing. Convolutional neural networks tend to be used for automatic feature extraction and illness prediction. On the other hand, KNN algorithms are used to calculate distance and find exact correspondence within the dataset to predict illness outcomes. The data is gathered from an overview of sickness symptoms, data on the daily habits of patients, and specifics from different doctor appointments. This will be able to help in evaluating the efficacy of the proposed system against several others, namely Random Forest, Naïve Bayes, Support Vector Machine, and Logistic Regression.