Disease Prediction using Machine Learning
| dc.contributor.author | Kammara Trivikram 1NH20CE022; Manobhi Ram Reddy B 1NH20CE026; Shashank S 1NH20CE045; Sushmitha H 1NH20CE053 | |
| dc.date.accessioned | 2025-05-31T08:55:58Z | |
| dc.date.available | 2025-05-31T08:55:58Z | |
| dc.date.issued | 2024 | |
| dc.description.abstract | In contemporary healthcare systems, the timely and accurate diagnosis of diseases represents a critical pillar for effective treatment and the containment of healthcare costs. Addressing this imperative, our project endeavors to develop a sophisticated web application that harnesses the power of machine learning algorithms to anticipate diseases based on user-provided symptoms. By doing so, we aim to facilitate prompt medical intervention and improve healthcare outcomes. Central to our solution is the implementation of an ensemble model that combines multiple machine learning classifiers, including Logistic Regression, Random Forest, KNN, MLP, and SVM, with XGBoost serving as the meta-learner. This ensemble approach capitalizes on the diverse strengths of these algorithms to achieve an exceptional accuracy rate of 97%. Through meticulous testing and validation procedures, we have ensured the reliability and efficacy of our predictive model. The core functionality of our Flask-based web application revolves around providing users with a seamless and intuitive platform for disease prediction and access to tailored healthcare recommendations. Key features include user authentication mechanisms to safeguard personal data, a user-friendly interface for inputting symptoms, and integration with the Google Maps API to offer personalized recommendations of nearby medical specialists. This integration enhances user experience by facilitating convenient access to healthcare services. One of the distinguishing aspects of our project is its emphasis on proactive healthcare management through early disease detection. By leveraging machine learning algorithms to analyze symptom datta, our application enables users to receive timely alerts regarding potential health risks. This proactive approach empowers individuals to take proactive measures to safeguard their health and seek medical assistance when needed, ultimately contributing to improved health outcomes | |
| dc.identifier.uri | http://192.168.75.5:4000/handle/123456789/19176 | |
| dc.language.iso | en | |
| dc.publisher | NHCE | |
| dc.title | Disease Prediction using Machine Learning | |
| dc.type | Learning Object |