Smart Health: A Machine Learning Approach to Predict Vector-Borne Diseases

Vector-borne diseases, like malaria, dengue, and chikungunya, are spread by insects like mosquitoes and ticks, posing significant health risks, particularly in tropical and subtropical regions. These diseases are especially prevalent in areas with limited access to healthcare, where they place a considerable burden on public health, with India experiencing millions of cases each year. On a global scale, these diseases account for over 17% of infections caused by pathogens such as bacteria, viruses, and parasites. Malaria, in particular, is responsible for around 219 million cases and more than 400,000 deaths annually. Detecting and controlling these diseases early is difficult because of factors like unpredictable weather, environmental changes, differences in regions, and varying socio-economic conditions. This research aims to address these complexities by analyzing historical data to predict the spread of vector-borne diseases in India. By employing advanced machine learning techniques, such as Random Forest and Logistic Regression, we have developed models that accurately forecast potential outbreaks. These models are crucial in identifying high-risk areas, enabling healthcare professionals to respond promptly and implement targeted interventions. By improving prediction accuracy, the study aims to enhance public health responses, optimize disease control measures, and contribute to more effective strategies for prevention.