Machine Learning for Risk Prediction of Colon and Lung Cancer

The Omdena team developed and deployed two machine learning enabled applications, one for lung cancer prediction and one for colorectal cancer (CRC) prediction. The user of the apps can enter the input data for a patient and get the likelihood of cancer as an output.

The partner for this project, Radmol AI, is a Dublin-based and Microsoft for Startups supported company on a mission to minimize the risk of delay and errors in medical diagnosis.

The problem

According to WHO (world health organization), 80% of countries do not have early detection programs/guidelines for childhood cancer while 67% of countries do not have a childhood cancer defined referral system.

As a result, in high-income countries, more than 80 percent of children diagnosed with cancer are cured, but this figure is only 20 percent in many emerging countries. The disparity is mostly the result of a late or inaccurate diagnosis, among others. Many cancer patients could be saved from premature death and suffering if they had timely access to early detection programs and adequate treatment.

Many cancer patients could be saved from premature death and suffering if they had timely access to early detection programs and adequate treatment. Radmol AI´s solution will facilitate early detection and prompt intervention, hence minimizing needless loss of lives and strain on the economic resources of individuals and countries.

The project outcomes

Within 10 weeks, the team covered the following steps:

• Collecting datasets for cancer patients with previous symptoms, demographics data, and ailments
• Labeling datasets appropriately
• Training and testing various machine learning models for lung and colorectal cancer (CRC) prediction
• Deploying two applications to visualize the predictions (see the screenshot below)