Technical director Naveen VK applied the power of machine learning algorithms to analyze organ transplant data and identify factors that may be related to successful outcomes for patients.
Naveen was provided a spreadsheet of 50,000 anonymized records of liver transplants and narrowed the information down to a few major data elements with an end goal of predicting survival time in years. She decided to use Python for this project with open source libraries like scikit-learn for machine learning models, NumPy to deal with large data arrays, and Pandas for analysis and manipulation of the data.
After utilizing and training the stochastic gradient descent regression available in the scikit-learn library, a straightforward yet effective model was created to predict patient survival time. The model was trained on features that included both patient and donor data to make the predictions. The resulting model scored at an impressive 96.7% accuracy with a root mean-square error of only 1.67, which further highlighted the precision of the trained model.
Naveen utilized the Matplotlib library to generate insightful visualizations and charts based on the selected features and the predicted data. These charts provided meaningful comparisons between actual and predicted survival time. She and some fellow nvisionaries explored potential correlations and impacts between survival time and various features like length of time on the waiting list, the age of the liver donor, the distance between donor and recipient, etc.
In many fields, including the medical sector, understanding and recognizing the connections between various data points may seem intuitive. However, data analysis like this can help to replicate and validate these intuitions as well as reveal new avenues for further exploration which in turn may lead to novel discoveries and solutions.
Meet the nvisionary
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Name: Naveen VK |
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