METHOD FOR DETECTING HEART DISEASE

Abstract

ABSTRACT A method (100) for detecting heart disease, the method (100) comprising the steps of collecting patient data, pre-processing the patient data, which includes handling missing values, normalizing data, and feature engineering to prepare the data for machine learning analysis, inputting the pre-processed data into a machine learning model trained to predict heart disease, generating a predictive output from the model indicating the probability of the patient having heart disease and displaying the predictive output on a user interface, providing healthcare professionals with an assessment of heart disease risk. <>

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to medical diagnostics, specifically to methods for detecting heart disease using machine learning models that analyse patient data to provide predictive outputs for risk assessment.
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] Cardiovascular disease remains one of the leading causes of mortality globally. Early detection and intervention are critical in reducing adverse cardiovascular outcomes. Conventional diagnostic methods for heart disease , Claims:1. A method (100) for detecting heart disease, the method (100) comprising the steps of:
collecting patient data;
pre-processing the patient data, which includes handling missing values, normalizing data, and feature engineering to prepare the data for machine learning analysis;
inputting the pre-processed data into a machine learning model trained to predict heart disease;
generating a predictive output from the model indicating the probability of the patient having heart disease; and
displaying the predictive output on a user interface, providing healthcare professionals with an assessment of heart disease risk.

2. The method (100) of claim 1, wherein the patient data includes demographic information, physiological measurements, medical history, and results of diagnostic tests.

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