A scalable solution for multi-symptom disease prediction and lifestyle recommendations using machine learning

Abstract

Abstract Accurate prediction of diseases from multiple co-occurring symptoms remains an important challenge in intelligent healthcare systems. Machine-learning models can support early symptom-based risk screening; however, their clinical use is limited by dataset quality, lack of external validation, interpretability concerns, and the risk of overstated diagnostic claims. This study presents a mobile decision-support prototype for multi-symptom disease prediction and rule-based lifestyle recommendation. The experiments were conducted using the publicly available Kaggle Medicine Recommendation System Dataset, which contains 4,920 symptom-based records spanning 41 disease classes, along with supporting files for disease descriptions, precautions, medications, dietary suggestions, and workout recommendations. Seven supervised machine-learning models were evaluated, including Decision Tree, Random Forest, Naive Bayes, Logistic Regression, XGBoost, Support Vector Machine, and K-Nearest Neighbors. The best-performing models achieved high classification accuracy under the adopted evaluation protocol. To avoid overinterpretation, these results are reported as benchmark performance on a secondary public dataset rather than evidence of clinical diagnostic validity. The Android-Flask prototype links the predicted disease class to a lookup-based recommendation layer that retrieves disease-associated information from supporting datasets. The system should therefore be interpreted as a decision-support and educational prototype, not as a clinically validated diagnostic or prescribing tool. Future work should include external validation in independent clinical cohorts, clinician assessment of recommendations, robustness testing under missing or noisy symptom data, and broader evaluation across real-world healthcare settings.