• M. Shiva
• M. Ganesh
• B. Niveditha
• K. Meghana
• L. Chandra Sekhar Reddy

The prediction of diseases based on symptomatic information has emerged as a pivotal area in healthcare, leveraging data-driven methodologies to enhance diagnostic accuracy. This abstract provides a concise overview of the techniques employed in disease prediction via symptoms. By harnessing machine learning, artificial intelligence, and data analytics, researchers and healthcare practitioners have achieved notable progress in forecasting diseases using patient-reported symptoms. This abstract underscore the significance of early detection and proactive management of healthcare through predictive models. Various data sources, including electronic health records, wearable devices, and patient self- reports, contribute essential symptom data for predictive modeling. Challenges associated with noisy and incomplete data are addressed, emphasizing data preprocessing and feature engineering to refine predictions. Machine learning algorithms, such as decision trees, support vector machines, and neural networks, are applied to construct predictive models. Incorporating clinical knowledge and domain expertise enhances model performance. Genetic, demographic, and environmental factors are integrated to bolster robustness. Ethical considerations encompassing patient privacy, data security, and potential biases are discussed, highlighting responsible model deployment. Transparent, interpretable AI techniques aid in deciphering model predictions for informed decision-making. In summary, disease prediction using symptoms offers a promising avenue for early intervention and personalized treatment. This abstract encapsulates the methodologies, challenges, and ethical implications surrounding symptom-based predictive models, emphasizing their potential to reshape healthcare through data-driven insights.

Machine learning, Decision Making, ML Technique

" A Review on Symptom Driven Disease Prediction Using ML Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.e860-e864, April-2024, Available :http://www.jetir.org/papers/JETIR2404496.pdf

" A Review on Symptom Driven Disease Prediction Using ML Algorithm", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppe860-e864, April-2024, Available at : http://www.jetir.org/papers/JETIR2404496.pdf