Volume 2 Number 1 2024
Machine Learning for Chronic Disease Predictive Analysis for Early Intervention and Personalized Care
Rabi Sankar Mondal 1*, Md Nazmul Alam Bhuiyan 1, Lamia Akter 2
Applied IT & Engineering 2(1) 1-8 https://doi.org/10.25163/engineering.2110301
Submitted: 02 January 2024 Revised: 26 March 2024 Published: 28 March 2024
Abstract
Machine learning (ML) is transforming healthcare, particularly in the prediction, diagnosis, and management of chronic diseases, including diabetes, cardiovascular conditions, cancer, and obstructive sleep apnea. By analyzing complex, large-scale datasets, ML enables earlier identification of at-risk individuals and supports personalized treatment planning, ultimately improving patient outcomes. Key ML techniques include supervised learning methods like support vector machines, logistic regression, decision trees, and random forests, along with deep learning models such as convolutional and recurrent neural networks. These are widely applied in predictive analysis across various chronic illnesses. ML is also driving innovation in drug development. For instance, platforms like DruGAN utilize generative adversarial networks to design novel drug candidates with targeted therapeutic properties, thereby significantly reducing the time and cost associated with traditional drug discovery. Furthermore, ML enhances clinical trials by optimizing patient recruitment and data analysis, accelerating the translation of research into clinical applications. Despite its promise, ML faces several challenges in healthcare integration. Algorithmic bias, often stemming from non-representative training data, can exacerbate health disparities. Addressing this requires diverse datasets, transparent model development, and continuous monitoring of bias. Data privacy is another primary concern, necessitating robust ethical frameworks and evolving regulations to protect patient information. Additionally, active patient engagement and interdisciplinary collaboration among clinicians, data scientists, ethicists, and administrators are essential to ensure the ethical, effective deployment of ML. In conclusion, While ML offers powerful tools for improving chronic disease care, its success depends on addressing technical and ethical challenges through fairness, transparency, and collaborative implementation.
Keywords: Machine Learning, Chronic Disease Management, Predictive Analytics, Personalized Healthcare, Algorithmic bias
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