METHOD FOR PREDICTING THE LIKELIHOOD OF HEART DISEASE IN AN INDIVIDUAL

Abstract

ABSTRACT A method (100) for predicting the likelihood of heart disease in an individual. Further, the method comprising collecting a dataset that includes demographic and physiological data of patients, including but not limited to age, gender, blood pressure, cholesterol levels, and medical history. Further, the method (100) comprising the steps of pre-processing the collected dataset to remove inconsistencies, handle missing values, and normalize the data for accurate analysis. Further, the method (100) comprising the steps of applying one or more machine learning algorithms to the pre-processed dataset to train a predictive model. The algorithms include at least one of a Support Vector Machine (SVM), a Random Forest, a Decision Tree, or a Logistic Regression. Further, the method (100) comprising the steps of generating a prediction of heart disease risk for an individual based on the trained model and providing actionable insights for preventive measures based on the prediction outcome.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of healthcare technology and, in particular, relates to a method for predicting heart disease risk using machine learning algorithms that analyse demographic and physiological data to enhance early detection and preventive healthcare strategies.
BACKGROUND
[0002] The subject matter discussed in the background section should not be assumed to be prior art merely as a result of its mention in the background section. Similarly, a problem mentioned in the background section or associated with the subject matter of the background section should not be assumed to have been previously recognized in the prior art. The subject matter in the background section merely represents different approaches, which in and of themselves may also correspond to implementations of the claimed technology.
[0003] Heart disease remains one of the leading causes of morbidity and mortality worldwide, posing significant challenges to public health systems. Tra , Claims:1. A method (100) for predicting the likelihood of heart disease in an individual, the method comprising the steps of:
collecting a dataset that includes demographic and physiological data of patients, including but not limited to age, gender, blood pressure, cholesterol levels, and medical history;
pre-processing the collected dataset to remove inconsistencies, handle missing values, and normalize the data for accurate analysis;
applying one or more machine learning algorithms to the pre-processed dataset to train a predictive model, wherein the algorithms include at least one of a Support Vector Machine (SVM), a Random Forest, a Decision Tree, or a Logistic Regression; and
generating a prediction of heart disease risk for an individual based on the trained model and providing actionable insights for preventive measures based on the prediction outcome.

2. The method (100) as claimed in claim 1, the pre-processing step further includes balancing the dataset to address class imbalances in the heart disease in

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