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ABSTRACT
Heart disease is a pervasive health concern affecting millions globally. Early detection and prediction of such ailments stand crucial for individual survival and effective management. This paper delves into the accurate anticipation of heart diseases by analyzing patient symptoms, employing diverse algorithmic models like naïve Bayes, random forest, logistic regression, and K-nearest neighbors (KNN).
The significance of these predictive models lies in their ability to sift through symptom data efficiently. Naïve Bayes, leveraging probability theory, categorizes symptoms based on their occurrence likelihood, while random forest, a robust ensemble learning method, amalgamates multiple decision trees to enhance predictive accuracy. Logistic regression, adept at binary classification problems, and KNN, relying on proximity to neighboring data points, offer varying perspectives and approaches in disease anticipation.
The crux lies not only in predicting the disease but in facilitating prompt action. Once the system predicts a potential heart condition, it transitions seamlessly to recommending the appropriate specialist to consult. This seamless integration between prediction and recommendation streamlines the process, ensuring timely access to specialized care, a pivotal factor in mitigating the disease’s impact.
Moreover, the development of an interactive interface stands as the cornerstone of user engagement and ease of access. This interface not only aids in symptom input but also seamlessly presents the predictive outcome and doctor recommendations.
