Prediction of patient adherence to medication using random forest, decision tree and neural networks techniques

Pathak, Sahil Shashikant (2024) Prediction of patient adherence to medication using random forest, decision tree and neural networks techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

Two serious health diseases such as diabetes and hypertension, which are very critical in nature and require constant attention and medication adherence which leads to symptoms in control and complications are reduced greatly. Despite the importance of following prescribed medication regimens, non-adherence is a significant problem among patients with these conditions. This leads to very desperate and poor quality health outcomes, it leads to expensive hospital costs and a burden on the healthcare system. This study aims to predict patient adherence to medication for diabetes and hypertension using various machine learning models as listed further below. Multiple datasets are being used and few models have performed better than other ones as well. By analyzing a range of patient data, including demographic, behavioral, and medical information, the research seeks to identify key factors that influence adherence and how to optimize it.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Staikopoulos, Athanasios UNSPECIFIED
Uncontrolled Keywords:	Adherence; Medication; Diabetes; Hypertension; machine learning
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science R Medicine > R Medicine (General) R Medicine > Healthcare Industry Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Data Analytics
Depositing User:	Ciara O'Brien
Date Deposited:	04 Sep 2025 09:33
Last Modified:	04 Sep 2025 09:33
URI:	https://norma.ncirl.ie/id/eprint/8771

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