Paper Contents
Abstract
This project focuses on the prediction of diseases using machine learning, with the goal of improving early detection and enhancing diagnostic accuracy in healthcare systems. By leveraging advanced machine learning algorithms such as random forests, decision trees, and support vector machines (SVM), the system effectively analyzes patient data to identify patterns and assess the risk of various diseases. These algorithms are capable of recognizing subtle correlations within complex medical data, enabling early identification of potential health issues before they progress to more severe stages. In order to optimize the performance of these models, techniques like feature selection and data preprocessing are implemented to improve the quality of input data and ensure more accurate predictions. Additionally, addressing common challenges such as data imbalances, where certain conditions may be underrepresented, is crucial to avoid biased outcomes. The models are trained on extensive healthcare datasets, incorporating a diverse range of variables, such as demographic information, medical histories, lifestyle factors, and test results. This approach demonstrates significant potential for automating the diagnostic process, allowing for quicker and more reliable identification of diseases. Moreover, it can assist in personalized treatment planning, offering tailored solutions based on individual patient profiles. The integration of machine learning in healthcare systems can lead to improved decision-making, enhanced patient outcomes, and more efficient public health management, marking a crucial step toward the future of precision medicine. Keywords : Machine Learning , Random Forest , Support Vector Machine , Artificial Intelligence , Naive Bayes.
Copyright
Copyright © 2024 Dhaman Kovachi. This is an open access article distributed under the Creative Commons Attribution License.
