SYSTEM FOR PREDICTING RISK OF HEART DISEASE

Abstract

ABSTRACT A system (100) for predicting risk of heart disease comprising a user interface (102) operated by a user to input patient data along with one or more patient attributes, a memory (104) integrated with the user interface (102), wherein the memory (104) is configured to store the patient data along with one or more patient attributes and one or more machine learning based prediction protocols and at least one processor (106) coupled to the user interface (102) and the memory (104), wherein the at least one processor (106) is configured to select at least one of the one or more machine learning based prediction protocols based at least on the one or more attributes to generate an output and predict a risk percentage of heart disease based at least on the output. <>

Specification

Description:[0001] This invention generally relates to field of heart diseases and in particular relates to a system for predicting risk 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 is one of the leading causes of mortality worldwide, impacting millions of individuals and posing a significant burden on healthcare systems. Effective early prediction of heart disease risk can enable timely intervention, reducing the severity of outcomes and potentially saving lives. Traditional methods for , Claims:1. A system (100) for predicting risk of heart disease, the system (100) comprising:
a user interface (102) operated by a user to input patient data along with one or more patient attributes;
a memory (104) integrated with the user interface (102), wherein the memory (104) is configured to store the patient data along with one or more patient attributes and one or more machine learning based prediction protocols; and
at least one processor (106) coupled to the user interface (102) and the memory (104), wherein the at least one processor (106) is configured to:
select at least one of the one or more machine learning based prediction protocols based at least on the one or more attributes to generate an output; and
predict a risk percentage of heart disease based at least on the output.

2. The system (100) as claimed in claim 1, wherein the one or more attributes include age, sex, chest pain type, resting blood pressure, cholesterol level, fasting blood sugar, resting electrocardiogram results, maximum heart r

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