Best AI & ML Projects for Heart Disease Prediction in 2025

Worldwide Cardiovascular diseases (CVDs) take the position as the leading cause of death since they represent one-third of total fatalities. India experiences a substantially higher rate of cardiovascular diseases than the worldwide standards because heart disease fatalities in the country are much more common than in other nations. Most cardiovascular deaths occur at a young age when individuals with hypertension and diabetes continue following unhealthy lifestyle patterns. The projected increase in cardiovascular death rates will be 91.2%, yet age-standardized cardiovascular mortality rates (ASMR) will decrease by 23.0% from 2025 to 2050.

With the help of AI-driven predictive analytics, we can now foresee potential health risks before they turn serious. These intelligent systems work as proactive digital health aides, guiding individuals through their healthcare journeys. By analyzing personal habits, medical histories, and genetic data, Artificial Intelligence tools can detect subtle warning signs of heart disease early on.

The following article examines top heart disease prediction projects utilizing Machine Learning and their key system components in detail.

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Top 5 Heart Disease Prediction Projects in 2025

With the rise of advanced technologies, heart disease prediction is becoming more accurate and accessible. Enrolling in a machine learning course can equip you with the skills to develop cutting-edge projects that have the potential to save lives.

Want to build impactful AI solutions like heart disease prediction tools? Explore our top courses designed for aspiring AI & ML professionals:

• Generative AI for Business Leaders – IIIT Bangalore
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1. AI-ECG Risk Estimation (Aire) by NHS England

Description: A “superhuman” AI tool will undergo testing within the NHS England system to determine future patient illness risks and mortality probabilities. AI-ECG risk estimation technology under the name Aire serves as a device to analyze electrocardiogram (ECG) test results. The examinations trace the electrical patterns of the heart so healthcare professionals can identify medical problems. This tool enables doctors to detect structural heart problems that perhaps escape their attention and simultaneously detect patients needing further care.

Key Features:

• Additional actionable predictions,
• Biological plausibility,
• Detailed explainability, and
• Integration with existing risk factors

Impact

A study reported in the Lancet Digital Health journal indicated that Aire was able to accurately determine a patient’s death risk within ten years post-ECG in 78% of instances.

Researchers trained Aire with a dataset containing 1.16 million ECG test results from 189,539 individuals.

The platform was also able to forecast future heart failure in 79% of instances, future severe heart rhythm issues in 76% of instances, and future atherosclerotic cardiovascular disease – where arteries constrict, hindering blood flow – in 70% of instances.

2. iCardo: A Machine Learning-Based Smart Healthcare Framework for Cardiovascular Disease

Description: Healthcare researchers developed a machine learning platform dedicated to CVD risk performance assessments using electrocardiograms as medical input for early heart condition recognition which produces innovative potential interventions.

Key Features

• Early detection
• Improved accuracy
• Personalized risk assessment
• Accessible screening

Impact: Examining medical information such as electrocardiograms (ECGs) to detect subtle trends that might reveal an increased risk of heart disease, frequently prior to obvious symptoms appearing.

3. Prediction4Protection: Machine Learning Application in Early Heart Disease Prediction

Description: Patients can utilize Prediction4Protection to supply their medical information including age, gender, blood pressure, cholesterol levels and family medical records for machine learning algorithms to produce heart disease prediction results. The system allows medical staff to initiate preventive measures before heart-related symptoms emerge which supports lowering the potential severity of cardiac conditions.

Key Features:

• Real-time data monitoring
• Risk scoring
• Predictive modeling
• Integration with existing systems
• Anomaly detection
• Automated response mechanisms
• Customizable alerts

Impact: Utilizing extensive medical data to recognize intricate patterns and more accurately forecast an individual's risk of developing heart disease.

4. MyBVI Smartphone Application

Description: The MyBVI Smartphone Application allows heart disease prediction through patient assessments of blood pressure and heart rate and physical activity measurements which it tracks using mobile devices. The analysis of algorithms in the application allows to estimate the cardiovascular risk of measured clinical information together with the life choices of patients, making possible rapid diagnosis and preventive actions.

Key Features:

• Real-time biometric data Collection
• Deploys ML algorithms
• Continuous monitoring of BP and heart rate
• Tailored insights to mitigate potential risk

Impact: BVI correctly identifies over 80 percent of patients who may be at risk for heart disease, stroke, or type 2 diabetes, showing a 23 percent enhancement compared to BMI and exceeding manual assessments. The tool provides a more accurate option compared to other indicators of disease risk, such as BMI and waist-to-hip ratio. "It may serve as an effective screening instrument for detecting metabolic syndrome."

5. Heart Disease Prediction Project by Shirin Shaik

Description: intends to employ machine learning techniques to examine a patient's medical information (such as age, sex, cholesterol levels, blood pressure, etc.) Healthcare organizations apply past patient data to create predictive models that identify heart disease risks in order to conduct early diagnosis along with preventive strategies according to possible risk elements.

Key Features:

• Demographic
• ECG readings
• Medical History
• Clinical measurements
• Lifestyle factors

Impacts:

An early cardiovascular exam system allows physicians to deliver timely interventions resulting in reduced deaths, better patient outcomes and enhanced resource management for healthcare while supporting patients to make positive lifestyle changes thus building up community well-being through pre-symptom risk detection.

Also Read: 12+ Machine Learning Applications Enhancing Healthcare Sector

Emerging Trends in Heart Disease Prediction

• Integration of Artificial Intelligence and Machine Learning

The integration of Artificial Intelligence with Machine Learning allows researchers to evaluate large patient medical record databases which include demographic data, clinical data, ECG results, imaging results, and lifestyle characteristics to reveal predictive heart disease patterns thus enabling identification for early interventions. This usually entails utilizing algorithms such as Logistic Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, and Neural Networks to produce precise predictions rooted in these patterns.

• Advancements in Genetic Research

Recent progress in genetic research for heart disease prediction initiatives encompasses: the capability to discover new genetic variants linked to cardiovascular disease (CVD) via extensive genome-wide association studies (GWAS), employing next-generation sequencing technologies to identify rare variants, merging genetic data with clinical information to generate enhanced risk scores, and investigating the possibilities of gene editing methods such as CRISPR-Cas9 for specific treatments; all of which support more precise evaluation of heart disease risk on an individual basis, facilitating personalized medicine strategies.

• Development of Accessible Diagnostic Tools

To create accessible diagnostic tools for predicting heart disease, prioritize user-friendly interfaces that can work with easily obtainable health data. Employ machine learning algorithms such as Logistic Regression, Random Forest, and Support Vector Machines (SVMs) using available patient information, including age, gender, blood pressure, cholesterol levels, and essential lifestyle elements, while maintaining data privacy and offering clear, comprehensible results for both patients and healthcare professionals.

Also Read: Top Artificial Intelligence Stats You Should Know About in 2025

How to Get Involved in Heart Disease Prediction Projects

• Educational Pathways

Approaches to tackle a top heart disease prediction project educationally might involve: utilizing accessible datasets, prioritizing data visualization and understanding, integrating ethical issues, detailing the functioning of machine learning algorithms applied, and stressing the significance of lifestyle changes informed by predictions as well as the project's technical elements.

• Skills and Technologies to Master

To successfully engage in heart disease prediction projects, you need to acquire expertise in data analysis, machine learning methods such as Logistic Regression, Support Vector Machines (SVM), Random Forest, Decision Trees, K-Nearest Neighbors (KNN), feature engineering, data cleaning, and model evaluation strategies, along with a good understanding of healthcare data sources and pertinent programming languages like Python and R.

• Contributing to Open Source Projects

Participating in open source projects for heart disease prediction is crucial as it speeds up research and development in this vital field by enabling researchers and developers to exchange data, algorithms, and insights, resulting in quicker progress in early detection, enhanced diagnostic precision, and ultimately, improved patient outcomes by making these tools broadly available and fostering collaborative enhancement.

Also Read: Types of Machine Learning Algorithms with Use Cases Examples

How upGrad will help You

upGrad can assist you in acquiring essential skills in machine learning, data analysis, and healthcare domain knowledge to create a system for top heart disease prediction project with machine learning. This can enable you to construct a model capable of examining patient data and detecting potential heart disease risks, facilitating early intervention and enhanced patient outcomes; by offering extensive courses on data cleaning, feature engineering, model selection, evaluation, and deployment.

Few ML & AI courses that you can benefit from include:

• Post Graduate Certificate in Machine Learning & NLP (Executive)
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To get started, students can reach out to upGrad for a free session and explore how they can unlock their potential in this exciting field.

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References: 
https://www.who.int/india/health-topics/cardiovascular-diseases 
https://www.thelancet.com/journals/lanwpc/article/PIIS2666-6065(24)00132-9/fulltext 
https://www.thescottishsun.co.uk/health/13664343/phone-app-mybvi-body-scan-risk-stroke-diabetes/?
https://www.polygence.org/projects/research-project-prediction4protection-machine-learning-application-in-early-heart-disease-prediction?