BIG DATA AND MACHINE LEARNING FRAMEWORK FOR CANCER, FINANCIAL, AND STRESS RISK PREDICTION

The rapid evolution of the big data in the medical, financial, and behavioral track has offered an opportunity to utilize predictive analytics to contribute to the well-being of the whole picture. However, the existing systems tend to work on the prediction of cancer risks, estimation of financial status, and stress analysis independently, which is limited to provide integrated and tailored risk measurement in the future. To address this limitation, the proposed paper will recommend one single Big Data and Machine Learning Framework to forecast the risks of Cancer, Financial, and Stress. It uses a combination of heterogeneous data, medical indicators, financial data, and behavioral data that are connected to stress and executes the supervised machine learning algorithms such as Logistic Regression, Decision Tree, Random Forest, Linear Regression, and Gradient Boosting. The results of the experiment indicate that, as compared to the Logistic Regression or the Random Forest, Decision Tree model predicted the risk of cancer the most accurately with an accuracy of 83%. Gradient Boosting was the lowest in the Mean Squared Error of 5.25 x 10-6 and better than that of Linear Regression, which is 0.15. In addition, stress risk classification was also effective in the determination of the various levels of stress basing on behavioral and physiological indicators. These results confirm the notion that the proposed integrated framework improves predictive accuracy and makes it possible to consider risks in the comprehensive manner. This model provides decision support tool, which is data-oriented to detect early risks of cancer, financial planning, and stress management to improve holistic well-being.