Multiple Disease Prediction based on User Symptoms using Machine Learning Algorithms
Keywords:
Decision trees, K-Nearest Neighbors (KNN), Logistic regression, Naïve bayes, Random forests, Support Vector Machines (SVM)Abstract
With the help of sophisticated machine learning techniques, we propose a system for predicting multiple diseases based on user-reported symptoms. To train the prediction models, the system utilizes a large dataset containing medical records and symptom-disease relationships. Algorithms such as Decision Trees, Support Vector Machines (SVM), Naïve Bayes, Logistic Regression, K-Nearest Neighbors (KNN), and Random Forests are employed to enable accurate analysis of input data. A custom backend handles data preprocessing and powers a user-centered interface for symptom entry. The trained models are stored to enable real-time predictions. Patient data confidentiality is ensured through a secure database that complies with healthcare information privacy standards. By using scalable platforms, healthcare professionals can perform accurate disease prediction, aiding early diagnosis and personalized treatment planning. This system enhances healthcare delivery, reduces costs, and improves patient outcomes. Symptom-based disease prediction, backed by machine learning, holds the potential to transform clinical decision-making.
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