AI is transforming many industries, especially the medical industry. Not only is it able to find patterns in data and imaging through neural networks and NLP, it is also able to help in the overall diagnosis and prognosis of a patient. Using AI combined with a physician's expertise, this could pave the way to bettering a human's overall survival and creating unique alternatives of preventing disease in the field of health and healing ❤️

This section specifically looks at predicting the mortality rate of patients more accuratly. This is done using decision trees to model non-linear relationships, especially when data on a patient may be missing.

• Linear Prognostic Models
• Prognosis with Tree-based Models
• Survival Models and Time
• Build a Risk Model Using Linear and Tree-based Models

Looking at the overall mortality rate amoung patients and estimate when disease may occur in a patient based off of factors such as blood pressure(BP), weight, if there is a mass, etc. Also, how to handle real-world issues when patient data has gone missing or may have never been put on record.