Published Paper Details:

The technology allows users to with the assistance of algorithms, users of the technology may predict whether or not they will develop diabetes mellitus and whether or not they will acquire cancer. This system uses a number of classification models, including association rules, logistical regression, artificial neural networks, decision trees, and Naive Bay. The accuracy of every model in the project is then determined using the Random Forest technique. The project is a smartphone application made to predict whether or not a person's class is at risk for diabetes and cancer. The dataset used is a Pima Indians Diabetes Data Set, which contains information on individuals, some of whom develop diabetes. We are investigating four popular classifiers for sickness risk prediction. These algorithms include Bay Naive, Regression Logistic, Artificial Neural Network, and Decision Tree. Subsequently, these algorithms are merged with bagging and boosting procedures to improve each model's solidity. Finally, the Random Forest algorithm is applied. The purpose of this research is to assess diabetes and cancer risk without requiring blood work or hospital stays for any individual. Encouraging and improving human health is another goal of the study.