Year: 2024
Type: article
Abstract: Predictive systems that detect illnesses early have been made possible by the introduction of machine learning (ML) in the healthcare industry. This has improved patient outcomes and allowed for prompt interventions. The goal of this research is to develop an ML-based system that integrates clinical, lifestyle, and demographic data to forecast diseases like diabetes. The method strikes a balance between accuracy and transparency by using models like Random Forest and XGBoost in conjunction with interpretability tools like SHAP. The outcomes show promise for practical implementation as they show a notable enhancement in prediction performance when compared to baseline techniques. This study emphasizes how machine learning (ML) might improve preventive healthcare and save treatment expenses.
Author: Akula Purna Adithya
Institution: Sathyabama Institute of Science and Technology
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4405890179