METHOD FOR PREDICTING 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 comprehensive dataset of patient information including demographics, medical history, lifestyle factors, and biomarkers. Further, the method (100) comprising the steps of pre-processing the dataset to handle missing values, encode categorical variables, and standardize numerical features. Further, the method (100) comprising the steps of applying multiple machine learning algorithms to the pre-processed dataset to develop predictive models. Further, the method (100) comprising the steps of evaluating the performance of each predictive model using metrics including accuracy, sensitivity, specificity, and area under the ROC curve. Further, the method (100) comprising the steps of selecting the most effective predictive model based on the evaluation metrics to provide risk assessments for heart disease.

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to the field of predictive healthcare analytics, specifically focusing on the development of a Heart Disease Prediction System utilizing machine learning algorithms. The aim is to harness diverse patient data including demographics, medical history, lifestyle factors, and biomarkers to accurately predict the likelihood of heart disease.
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 , Claims:1. A method (100) for predicting the likelihood of heart disease in an individual, the method comprising the steps of:
collecting a comprehensive dataset of patient information including demographics, medical history, lifestyle factors, and biomarkers;
pre-processing the dataset to handle missing values, encode categorical variables, and standardize numerical features;
applying multiple machine learning algorithms to the pre-processed dataset to develop predictive models;
evaluating the performance of each predictive model using metrics including accuracy, sensitivity, specificity, and area under the ROC curve; and
selecting the most effective predictive model based on the evaluation metrics to provide risk assessments for heart disease.

2. The method (100) as claimed in claim 1, wherein the machine learning algorithms comprise logistic regression, support vector machines, random forests, and neural networks, and wherein the evaluation metrics further include precision and F1-score.

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