Tumor growth simulation

Inspiration

My grandma died of cancer at age 60, it started as a tumor that posed very little threat to her life, so she overlooked it, she later learned that the cancer grew and was cancerous. She later died because of cancer. If my grandma tried to fight the tumor she would have prevented it from spreading. So if we can educate as many as possible about cancer and the dangers of tumors then we can potentially save a life.

What it does

A simulation of tumor growth with factors such as medicine, chemotherapy, and other forms of treatment and their effects on tumors, incorporating mathematical formulas and equations to find growth

Quick disclaimer, this project is not designed to be and is not intended for usage in actual medical situations. Some formulas and numbers are heavily generalized, as things like chemotherapy are a process with a certain success rate, and we tried our best to simulate as accurately as possible given a certain time span. It is also important to note that the treatment methods shown are not for anyone, and this simulation is purely for demonstration only.

The code runs as follows: (choose option a and then c) the patient’s tumor experiences a 5% growth per month, and the user has three choices: commercial drugs, chemotherapy or doing nothing. Each day that commercial drugs are used or nothing is done, the tumor increases. (choose option b) When chemotherapy is used, the rate of increase is slowed down by an initial 2-3%, with a 2% decrease every time it is used afterwards. The user then receives a random fact about tumors.

If the tumor grows too large, above 100%, then the game ends (reset and demonstrate a fail)

If the tumor shrinks down to 20%, the user can now choose surgery or radiation therapy. Since radiation therapy compliments surgery, the user has a 50% of success when using surgery alone, and 80% with radiation therapy beforehand.

The tumor_size_calc() function works with the parameters, tumor size and rate. Tumor size is the parameter both used and operated on and returned. Rate is modified outside of the code with the variable chemo_rate decreasing its effectiveness.

How we built it

We used the language Python and coded on Replit.

Challenges we ran into

We both know a couple of languages and it has been a while since we used Python, thus syntax was a minor issue. Are code was not too long but was long enough to pose organization issues, since we were working in a team, but we overcame this by creating a labeling system that we both understood. We were able to time manage well, both of us are really busy with other commitments but we managed.

Accomplishments that we're proud of

We are proud of our organization, time management, and efficiency. We came up with the idea, planned it out, and coded it in a couple of hours while managing other plans/commitments we had.

What we learned

The biggest thing we learned is teamwork, this was the first time both of us worked on a code with another person, so overcoming the challenges it posed was difficult but rewarding. We learned more about tumors and cancer then we though we would have as well.