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@article{206837,
        author = {Sanjana N N and Sowmya and Kavana H C and Shravya},
        title = {A Machine Learning Framework for Preliminary Disease Diagnosis from Patient Reported Symptoms},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {638-641},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206837},
        abstract = {Early diagnosis plays a pivotal role in providing proper treatment to the patient. However, many individuals fail to consult a physician due to reasons like unavailability of hospitals near them, lack of sufficient time or high expenses incurred by treatments. In this research paper, an approach is presented to make an early prediction of diseases by analyzing the symptoms input by patients using machine learning techniques. The suggested framework is based on supervised learning algorithms which include Random Forest Classifier, Decision Tree Classifier and Logistic Regression. A list of diseases is provided which includes Malaria, Dengue, Typhoid, Viral Fever, Common Cold, Urinary Tract Infection (UTI). Symptoms entered by user are then transformed into numerical values followed by disease prediction. In addition to the web interface designed in Flask, it provides preventive steps for better health to the patient. Experimental results have shown high performance of the model.},
        keywords = {Machine Learning, Disease Diagnosis, Random Forest, Healthcare, Symptom Prediction, Flask},
        month = {July},
        }