Abstract

The diagnosis of diseases at an earlier stage is critically important to the overall improvement of healthcare outcomes because it paves the way for timely interventions and more effective treatment choices. The development of the Smart Health Prediction System as a subfield of medical science has paved the way for reliable disease prediction by utilizing data mining strategies. This was previously impossible. However, the currently available systems have a number of drawbacks, including a slow generation rate for analysis reports, low operational efficiency, high operational costs, and poor accuracy. In this context, the model that is being suggested tries to properly forecast the overall health condition of individuals by leveraging measurable parameters such as TH11, Spo2, pulse rate, and MQ2 parameters. Specifically, the goal of this work is to improve overall health outcomes.