MACHINE LEARNING-BASED PREDICTIVE MODELS FOR EARLY DIAGNOSIS OF HEART DISEASE AND BREAST CANCER

Abstract

ABSTRACT A method (100) for predicting the risk of heart disease and breast cancer in patients is disclosed. Further, the method comprising collecting patient data from electronic health records, including demographic information, medical history, lifestyle factors, and genetic data. Further, the method (100) comprising the steps of pre-processing the collected data to ensure quality and consistency, including handling missing values and normalizing data formats. Further, the method (100) comprising the steps of analysing the pre-processed dataset using one or more machine learning algorithms to identify patterns and risk factors associated with heart disease and breast cancer. Further, the method (100) comprising the steps of generating a risk score for each patient based on the outcomes of the analysis. Further, the method (100) comprising the steps of providing a user-friendly interface for healthcare professio

Specification

Description:FIELD OF THE DISCLOSURE
[0001] This invention generally relates to a field of healthcare informatics and predictive analytics and in particular relates to a method for developing and implementing machine learning algorithms for the early diagnosis and prediction of chronic diseases, specifically heart disease and breast cancer.
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 and breast cancer are two of the leading causes of morbidity and mortality worldwide, significantly impacting public health , Claims:1. A method (100) for predicting the risk of heart disease and breast cancer in patients, comprising:
collecting patient data from electronic health records, including demographic information, medical history, lifestyle factors, and genetic data;
pre-processing the collected data to ensure quality and consistency, including handling missing values and normalizing data formats;
analysing the pre-processed dataset using one or more machine learning algorithms to identify patterns and risk factors associated with heart disease and breast cancer;
generating a risk score for each patient based on the outcomes of the analysis; and
providing a user-friendly interface for healthcare professionals to input patient data and receive risk assessments.

2. The method (100) as claimed in claim 1, wherein the machine learning algorithms include at least one of a logistic regression, decision trees, or neural networks, and further comprising the step of validating the risk score using a separate validation dataset to ensu

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