Advancing Healthcare Predictive Analytics: AIDriven Diagnosis and Real-Time Monitoring

Abstract

Predictive healthcare analysis has become an important technology that has brought many changes to the healthcare sector. It helps in detecting the patients’ conditions and deadly diseases at an early stage and help them to recover at a faster rate. This analysis method includes gathering a large amount of information regarding the patients’ past health records, current symptoms, and the surroundings in which the user lives. In addition to this information, it also collects information from wearable gadgets like smart watches and other medical equipment using tools like decision trees and neural networks. But these tools often struggle with the unstructured and unpredictable nature of the healthcare records and data. Here comes the use of our proposed method that overcomes this problem by using better ways to handle features and AI- powered tools for sorting out that unstructured information. It takes detailed information about the patient, like their surroundings, present condition, whether they are someone who travels a lot, or whether their family has any hereditary issues, that is, genetic issues and all to make much precise prediction. The AI algorithm helps by taking in all these details and cutting down the possibilities of what the patient might be suffering from. On top of that, this method will consistently keep tracking data from wearable gadgets, gives the algorithm more details about view of patient’s health condition. Combining these new features with the existing approaches helps in diagnosing more accurately by using medical history with habits, climate, and DNA. These changes aren’t just improving how we are able to easily detect diseases at an early stage, but they are also providing a way for healthcare professionals to quickly respond and focus on individuals.

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