A Review of Machine Learning Techniques for Symptom-Based Disease Prediction

Abstract

Symptom-based disease prediction is crucial in healthcare. It addresses the need for early diagnoses and effective treatment plans. Traditional diagnostic methods rely on human expertise, which can beslow and error-prone. Theuse of Machine Learning and Deep Learning techniques has greatly improved automated disease prediction systems. These systems provide faster and more accurate predictions. Still, there are ongoing challenges. These include issues with incomplete symptom data, improving prediction accuracy for rare conditions, and ensuring that models are easy to understand. This survey paper reviews current research in symptom-based disease prediction models. It focuses on Machine Learning algorithms, hybrid approaches, and data preprocessing methods. It also discusses recent advances in neural networks and Natural Language Processing for symptom analysis. Additionally, the paper proposes a framework that uses multi-modal data fusion to provide more reliable predictions. The review highlights technological innovations, challenges, and potential future applications in healthcare.

Introduction

Early and accurate disease diagnosis is essential for effective treatment and improved patient outcomes. Traditional diagnosis relies heavily on doctors' expertise, making it time-consuming and inconsistent, especially for diseases with overlapping symptoms. To address these challenges, Machine Learning (ML) and Artificial Intelligence (AI)-based disease prediction systems have been developed. However, they still face major issues such as incomplete or noisy symptom data, difficulty predicting rare diseases due to limited data, and lack of explainability, which is crucial for gaining clinicians' trust.

The literature shows that traditional ML techniques, including Decision Trees, Support Vector Machines (SVM), Random Forests, and k-Nearest Neighbours (k-NN), are commonly used for symptom-based disease prediction but struggle with complex and imbalanced datasets. Deep Learning models, such as Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), improve prediction accuracy by identifying complex symptom patterns, although they require large datasets and may overfit on small or imbalanced data.

Researchers have proposed several methods to improve data quality, including data imputation using k-means clustering, autoencoders, and anomaly detection to handle missing and noisy symptom data. For rare disease prediction, techniques such as ensemble learning, oversampling, and transfer learning have been introduced to improve classification despite limited training data.

To increase trust in AI-based predictions, Explainable AI (XAI) methods like LIME and SHAP have been used to show how individual symptoms contribute to predictions, although they require significant computational resources. Recent studies also integrate multi-modal data, including patient history, medical images, genetic information, and Electronic Health Records (EHRs), to improve diagnostic accuracy, but these approaches demand high computational power and face challenges in processing large, high-dimensional datasets in real time.

The methodologies reviewed include traditional ML algorithms for disease classification, data imputation techniques to manage missing and noisy symptom data, and noise reduction methods to improve input quality. While these approaches enhance prediction accuracy, they still have limitations in handling large-scale, diverse datasets and complex disease scenarios.

Conclusion

This survey paper has systematically reviewed the current researchonautomatedsymptom-baseddiseaseprediction.It highlights significant improvements in machine learning algorithms,datapreprocessingtechniques,andeffortstomake models more understandable. The system proposed in this review directly addresses critical challenges noted in the literature, including: • Effectivelymanagingincompletesymptomdata. • Increasingtheaccuracyofpredictionsforrarediseases. • Ensuringtheclarityandtransparencyofpredictions. By using data-driven methods, the proposed framework greatly improves the accuracy and reliability of automated disease prediction, offering valuable support to healthcare workers. While some challenges remain, such as integrating multimodal data and optimizing real-time prediction perfor-mance, the system shows promise for addressing the growing complexities of automated clinical diagnosis.

References

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Copyright

Copyright © 2026 Prafulla B. Bhand, Aniruddha N. Rane, Ayush K. Khairnar, Soham S. Phatak, Punam A. Shinde, Dr. Emmanuel M.. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.