Machine Learning-Based Hybrid Framework for Heart Disease Prediction 

The healthcare client wanted to address the access gap for primary healthcare in rural areas. Sparity designed a Telehealth module and AI-powered prediction model that detect unusual patterns in a heart-related emergency.

About Company

The healthcare client is a remote healthcare system provider that offers an in-clinic experience for doctors and patients using connected care solutions to bridge the gap. The healthcare client provides a connected care portfolio consisting of a full-stack offering including remote monitoring devices deployed in ICU’s, teleconsultation capabilities, a cloud-hosted EHR repository, remote OPD, Home Healthcare Solutions, and Provides Cost-Effective and Differentiated Solutions.

Machine Learning-Based Hybrid Framework for Heart Disease Prediction


The healthcare client approached Sparity intending to enhance their remote monitoring capabilities and needed a solution that bridges the access gap for primary healthcare in rural communities impacting the health outcome of many millions globally. Sparity, with its extensive experience in developing healthcare solutions for large healthcare organizations, designed a Telehealth module that can help plug the gap and assist in remote Doctor Consultation via telemedicine. Furthermore, it developed an AI-powered prediction model that can accurately monitor and analyzes critical vitals of the patient continuously from its Wi-Fi-enabled portable remote monitoring device and detect unusual patterns in a heart-related emergency.


Contemporary healthcare delivery is structured around physical interaction between the physician and patient, be it in clinics or hospitals. Its overall approach is excessively provider-centric and rarely focuses on the patient’s actual needs. Furthermore, underserved communities, particularly in rural areas, continue to experience inadequate primary healthcare. The healthcare client needed a solution that could bridge the huge access gap for primary healthcare in Underserved rural communities that can predict and assist patients in heart-related emergencies.


Machine Learning-Based Hybrid Framework for Heart Disease Prediction

AI-powered prediction model accurately analyzes PQRST waves through open CV libraries to detect abnormalities in ECG and other heart related emergency incidents for example Angina vs. Myocardial Infarction

Machine Learning-Based Hybrid Framework for Heart Disease Prediction

The solution gathers data from monitoring devices and analyze it for unusual patterns that differ from the expected behavior and detect patterns in the cardiovascular risk factors through a supervised learning approach.


Sparity’s healthcare product experts, based on initial requirements, developed a hybrid Web & Mobile application to enable hospitals to conduct hassle-free virtual consultations for remote multispecialty OPD. Sparity developed an AI-powered prediction model that analyzed different vitals of the patients that assist healthcare providers in predicting the risk of cardiac disease and initiating early intervention to make a real difference. The prediction model provides a risk score that incorporates lifestyle factors including diet, smoking preferences, physical activity, and psychological stress and anxiety reflected in the rate of respiration and hypertension. Furthermore, the solution allows remote monitoring and analyzing of patients’ vitals at the point of care and connects the caregiver and remote physician synchronously via the Internet using a teleconsultation interface for the purpose of resolving medical issues. 


Key Deliverables

Clinician’s assistance 

AI powered real-time prediction model assist physicians in predicting heart-related emergency


Enhanced healthcare delivery radius and traceability, thereby increasing coverage.

Smart insights 

Preventive diagnosis with intelligent and data-driven decisions offers a smarter patient experience 


Achieved higher levels of accuracy in handled Risk/Emergency situations 

Client Results

The impact we’ve had

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