Key result
NLP and Random Forest system stratifies heart disease risk from plain-language symptoms without laboratory tests.
Why the study?
Existing machine learning systems for predicting heart disease depend on structured clinical inputs obtained from medical tests, which restricts their use in rural and resource-limited settings.
Does an NLP-driven machine learning system enable preventive heart disease risk screening from plain-language symptom descriptions without requiring structured laboratory or clinical test inputs?
Does an NLP-driven machine learning system enable preventive heart disease risk screening from plain-language symptom descriptions without requiring structured laboratory or clinical test inputs?
Answers are grounded in this paper and cite their sources.
Proposes an NLP front-end for machine learning heart disease prediction so non-medical users in resource-limited settings can perform preventive triage using plain-language symptoms.
Cardiovascular disease (CVD) is behind nearly 32 percent of deaths worldwide, which makes early detection essential. Recent work applies machine learning and deep learning methods, including Decision Trees, Random Forest, XGBoost, Gradient Boosting, SVM and hybrid neural networks, to predict heart disease from clinical attributes such as age, blood pressure, cholesterol and ECG readings, with reported accuracies ranging from 65 percent to above 98 percent. These systems, however, depend on structured clinical inputs that cannot be obtained without medical tests, which restricts their use in rural and resource-limited settings. This paper surveys recent studies on heart disease prediction and draws on their findings to design an AI-Driven Heart Disease Prediction System that removes this dependency. The proposed system uses natural language processing to pull symptoms out of plain-language descriptions and assigns a Low, Medium, or High risk level with Random Forest and Logistic Regression models, offered through a Streamlit interface so that non-medical users can carry out preventive screening.
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Ullas et al. (2026) studied Cardiovascular disease / Heart disease. AI-Driven Heart Disease Prediction System using Natural Language Processing (spaCy) and Machine Learning (Random Forest and Logistic Regression) vs. Logistic Regression baseline and surveyed machine learning models requiring structured clinical inputs was evaluated on Heart disease risk classification (Low, Medium, or High risk) evaluated by accuracy, precision, recall, and F1-score. The proposed NLP and Random Forest system maps plain-language symptoms to UCI/Kaggle attributes to assign Low, Medium, or High heart disease risk without requiring laboratory tests.
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