Published Paper Details:

One disease at a time is the focus of many of the machine learning models for healthcare analysis currently in use. For example, one analysis is done for cancer, another for heart disease, etc. Multiple diseases cannot be predicted using the same framework by a single analysis. This study proposes a system to forecast various diseases using the Flask Application programming interface (API). The approach for examining diabetes, heart disease, kidney, liver, and cancer was proposed in this work. Numerous disease analyses were implemented using TensorFlow, the Flask API, and machine learning techniques. Python dataset is used to save the model behavior and to load the dataset file as necessary. The importance of this article analysis lies in the fact that all the contributing elements to the illness are taken into account while analyzing it, allowing for the identification of the disease's complete range of potential effects. For example, a lot of the existing techniques for analyzing diabetes took into account a few factors like age, sex, BMI, and insulin. However, in contrast to the existing model, our proposed system takes into account additional variables like the number of pregnancies, blood glucose levels, skin thickness, heart rate, and family history of diabetes. The behavior of the finished model will be captured in a Python dataset file, and the Flask API will be developed. When using this API, the disease's parameters must be sent along with the disease's name. The Flask API will call the pertinent model and then return the patient's status. The goal of this project is to look at as many diseases as we can in order to monitor patients' status and provide them warnings, when necessary, in order to lower the death rate