Multiple Disease Detection using Machine Learning
Abstract
The project ”Multiple Disease Detection using Machine Learning” aims to develop a system for the accurate and efficient detection of multiple diseases using machine learning algorithms. The system is designed to analyze patient data, including medical history, symptoms, and test results, and predict the likelihood of several diseases simultaneously. The project involves data pre processing, feature selection, and model training using various machine learning techniques such as decision trees, random forests, and support vector machines. The performance of the developed system is evaluated based on metrics such as accuracy, precision, recall, and F1-score using a dataset of patients with multiple diseases. The results of this project have the potential to improve the accuracy and efficiency of disease diagnosis, leading to better patient outcomes and reduced healthcare costs
Keywords:
Support Vector Machine, Logistic Regression, Disease Prediction, Accuracy, Precision
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