Efficient Disease Prediction Framework to Suggest Early Treatment Decisions in Healthcare

Abstract

This study proposes a novel framework for disease prediction intended to aid healthcare providers in making early treatment decisions. In order to efficiently identify early disease indicators and provide prompt suggestions for treatment interventions, the framework incorporates machine learning algorithms, clinical data analysis, and predictive modeling techniques (Smith, Johnson, & Davis, 2023). For the purpose of creating the framework, a comprehensive dataset made up of clinical records, demographic data, and test results from a large patient cohort was gathered. The dataset was analyzed using a variety of machine learning methods, and predictive models for various diseases were created. As evaluation criteria, accuracy, precision, recall, and F1-score were used to gauge how well these models performed. The experimental findings show that the suggested framework has a high degree of disease prediction accuracy. Additionally, by recommending early treatment selections. The approach offers the potential to improve patient outcomes and lower healthcare expenditures based on anticipated illness outcomes. The study benefits healthcare professionals, policymakers, and academics looking to use data-driven strategies for better disease management and patient care by providing an effective method for disease prediction and early treatment decisions.