What is it about?
This paper highlights the comparative analysis of various machine-learning algorithms for illness prediction. Accurate predictive data analysis from healthcare and pharmaceutical databases facilitates the diagnose of diseases for patient treatment and preventive measures.
Featured Image
Photo by National Cancer Institute on Unsplash
Why is it important?
This paper highlights the comparative analysis of machine-learning algorithms such as the Random Forest, Decision Tree, K-Nearest Neighbor, Naive Bayes, Support Vector Classifier, and Convolutional Neural Network for illness prediction. Accurate predictive data analysis from the healthcare and pharmaceutical databases can support early disease detection and patient treatment.
Perspectives
Predictive disease analysis is a cornerstone of modern healthcare, leveraging data and machine-learning to anticipate the likelihood developing certain diseases or conditions. By identifying potential risks early on, healthcare providers can implement preventive measures to mitigate the onset of diseases and strategize treatment plans. Early detection can lead to timely interventions, potentially limiting the severity of illnesses and reducing overall healthcare costs.
Angela Amphawan
Sunway University
Read the Original
This page is a summary of: A comparative study of machine learning techniques for accurate disease prediction using symptom-based diagnosis, January 2024, American Institute of Physics,
DOI: 10.1063/5.0217579.
You can read the full text:
Contributors
The following have contributed to this page
