Development of Early Leukemia Risk Detection System Using Machine Learning Algorithms

Ramadana, Afi Zain (0004) Development of Early Leukemia Risk Detection System Using Machine Learning Algorithms. Undergraduate thesis, Politeknik Negeri Jember.

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Abstract

Leukemia, an oncological condition affecting the blood-forming tissues of the body, often goes undiagnosed in its early stages due to the presence of non-specific symptoms, resulting in delayed treatment and elevated mortality rates. This study aims to develop a web-based system that utilises machine learning algorithms to detect early leukemia risks based on user-reported symptoms and health data.The proposed system employs K-means clustering and autoencoders neural network to predict risk levels (low, moderate, or high) while providing personalised recommendations and appointment reminders for medical consultation. The system utilises qualitative research methodologies, encompassing expert interviews and thematic analysis, to identify critical symptoms and user requirements. Employing an Agile development framework, the system was designed to ensure usability and accessibility for the general public, particularly in regions with limited healthcare resources. Key features include user-friendly interfaces, secure data handling, and an integrated prediction model capable of processing diverse datasets. Preliminary results indicate that the system effectively identifies leukemia risk with high accuracy, empowering users with actionable insights and facilitating timely medical interventions.This innovation underscores the potential of machine learning in enhancing early disease detection and improving healthcare accessibility, contributing to global efforts in reducing leukemia-related mortality. Keywords: Leukemia, Machine Learning, K-Means Clustering, Artificial Intelligence, Early Deases Detection

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