Weather Based Disease Prediction using Machine Learning

Author

D.Blessy, E Julie Ruth


Abstract

 Weather conditions such as temperature, humidity, and wind speed can influence human health and may contribute to the occurrence and severity of certain diseases. Early identification of potential health risks associated with changing weather conditions can support preventive awareness and timely healthcare decisions. This paper presents a Weather Based Disease Prediction Using Machine Learning system that integrates real-time weather information with patient details and symptoms to predict possible diseases. The proposed system collects patient information such as age and gender, selected symptoms, and weather parameters obtained through the OpenWeather API for the selected district or city. The collected data is preprocessed and transformed into suitable features for machine learning-based prediction. Several classification algorithms, including Decision Tree, Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest, are evaluated to identify an effective prediction model. Based on the experimental results obtained using the project dataset, Random Forest achieves the highest prediction accuracy among the evaluated models and is selected for the final prediction system. A Streamlit-based interactive application is developed to provide a user-friendly interface for entering patient information, selecting symptoms, viewing current weather conditions, and generating disease predictions. The system presents the predicted disease with a risk percentage, weather visualizations, recommendations, disease description, and prevention measures. The proposed approach demonstrates the potential of combining machine learning with real-time weather data to support disease prediction and health awareness. The system is intended as a decision-support and awareness tool and does not replace professional medical diagnosis or treatment.



Keywords

Machine Learning, Disease Prediction, Weather Data, Random Forest, OpenWeather API.



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References


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