Mohan Periyasamy 👋
A Passionate Software Developer 🖥️ & Aspiring SOC Analyst 🔐 having 2+ years of experience in Full Stack Development, Cybersecurity & Data Analysis across India.
🫀 Visualizing and Predicting Heart Disease Using Machine Learning
Heart disease remains a leading cause of death worldwide, making early diagnosis and prevention critically important. This project, developed by Mohan Periyasamy, combines software development and data science to provide a user-friendly tool that predicts heart disease risk based on key health indicators.
Why Machine Learning?
Traditional diagnosis methods can be time-consuming and may require complex tests. Machine learning (ML) offers an innovative approach by training algorithms on historical medical data to recognize patterns that signal potential heart disease risk. This predictive ability supports doctors in making faster and more accurate decisions.
Data Features Used
- Age: Older age generally correlates with higher heart disease risk.
- Resting Blood Pressure (BP): High blood pressure strains the heart, increasing risk.
- Cholesterol Levels: Elevated cholesterol can cause artery blockages.
- Maximum Heart Rate Achieved: Lower max heart rate may indicate heart problems.
How It Works
Using these features, machine learning models like Logistic Regression and Random Forest are trained on datasets containing patient information and their diagnosis outcomes. The models learn to classify inputs into risk categories: low, moderate, or high risk.
Our tool simulates this predictive capability on the frontend: users enter their own health data, and the tool applies simple logic inspired by typical ML model behavior to estimate risk instantly, providing educational insights and encouraging early medical consultation when needed.
Visualization and Interpretation
Beyond prediction, the project emphasizes visualization techniques:
- Correlation Heatmaps: Reveal which features most strongly impact heart disease.
- Boxplots and Distribution Graphs: Show the spread and variation in data, helping understand typical vs. abnormal ranges.
Impact on Healthcare
This project highlights how combining software engineering and data science can empower users and healthcare providers with accessible, data-driven tools. It advocates preventive care, raising awareness about heart health risks and prompting timely doctor visits — a crucial step toward reducing heart disease complications and fatalities.
Final Thoughts
While not a substitute for professional medical advice or diagnosis, this tool serves as an educational platform to demonstrate the power of machine learning in healthcare. It encourages users to take control of their health through data awareness and timely action.
Mohan Periyasamy 👋
A Passionate Software Developer 🖥️ & Aspiring SOC Analyst 🔐 having 2+ years of experience in Full Stack Development, Cybersecurity & Data Analysis across India.
🫀 Visualizing and Predicting Heart Disease Using Machine Learning
Heart disease remains a leading cause of death worldwide, making early diagnosis and prevention critically important. This project, developed by Mohan Periyasamy, combines software development and data science to provide a user-friendly tool that predicts heart disease risk based on key health indicators.
Why Machine Learning?
Traditional diagnosis methods can be time-consuming and may require complex tests. Machine learning (ML) offers an innovative approach by training algorithms on historical medical data to recognize patterns that signal potential heart disease risk. This predictive ability supports doctors in making faster and more accurate decisions.
Data Features Used
- Age: Older age generally correlates with higher heart disease risk.
- Resting Blood Pressure (BP): High blood pressure strains the heart, increasing risk.
- Cholesterol Levels: Elevated cholesterol can cause artery blockages.
- Maximum Heart Rate Achieved: Lower max heart rate may indicate heart problems.
How It Works
Using these features, machine learning models like Logistic Regression and Random Forest are trained on datasets containing patient information and their diagnosis outcomes. The models learn to classify inputs into risk categories: low, moderate, or high risk.
Our tool simulates this predictive capability on the frontend: users enter their own health data, and the tool applies simple logic inspired by typical ML model behavior to estimate risk instantly, providing educational insights and encouraging early medical consultation when needed.
Visualization and Interpretation
Beyond prediction, the project emphasizes visualization techniques:
- Correlation Heatmaps: Reveal which features most strongly impact heart disease.
- Boxplots and Distribution Graphs: Show the spread and variation in data, helping understand typical vs. abnormal ranges.
Impact on Healthcare
This project highlights how combining software engineering and data science can empower users and healthcare providers with accessible, data-driven tools. It advocates preventive care, raising awareness about heart health risks and prompting timely doctor visits — a crucial step toward reducing heart disease complications and fatalities.
Final Thoughts
While not a substitute for professional medical advice or diagnosis, this tool serves as an educational platform to demonstrate the power of machine learning in healthcare. It encourages users to take control of their health through data awareness and timely action.
Mohan Periyasamy 👋
A Passionate Software Developer 🖥️ & Aspiring SOC Analyst 🔐 having 2+ years of experience in Full Stack Development, Cybersecurity & Data Analysis across India.
🫀 Visualizing and Predicting Heart Disease Using Machine Learning
Heart disease remains a leading cause of death worldwide, making early diagnosis and prevention critically important. This project, developed by Mohan Periyasamy, combines software development and data science to provide a user-friendly tool that predicts heart disease risk based on key health indicators.
Why Machine Learning?
Traditional diagnosis methods can be time-consuming and may require complex tests. Machine learning (ML) offers an innovative approach by training algorithms on historical medical data to recognize patterns that signal potential heart disease risk. This predictive ability supports doctors in making faster and more accurate decisions.
Data Features Used
- Age: Older age generally correlates with higher heart disease risk.
- Resting Blood Pressure (BP): High blood pressure strains the heart, increasing risk.
- Cholesterol Levels: Elevated cholesterol can cause artery blockages.
- Maximum Heart Rate Achieved: Lower max heart rate may indicate heart problems.
How It Works
Using these features, machine learning models like Logistic Regression and Random Forest are trained on datasets containing patient information and their diagnosis outcomes. The models learn to classify inputs into risk categories: low, moderate, or high risk.
Our tool simulates this predictive capability on the frontend: users enter their own health data, and the tool applies simple logic inspired by typical ML model behavior to estimate risk instantly, providing educational insights and encouraging early medical consultation when needed.
Visualization and Interpretation
Beyond prediction, the project emphasizes visualization techniques:
- Correlation Heatmaps: Reveal which features most strongly impact heart disease.
- Boxplots and Distribution Graphs: Show the spread and variation in data, helping understand typical vs. abnormal ranges.
Impact on Healthcare
This project highlights how combining software engineering and data science can empower users and healthcare providers with accessible, data-driven tools. It advocates preventive care, raising awareness about heart health risks and prompting timely doctor visits — a crucial step toward reducing heart disease complications and fatalities.
Final Thoughts
While not a substitute for professional medical advice or diagnosis, this tool serves as an educational platform to demonstrate the power of machine learning in healthcare. It encourages users to take control of their health through data awareness and timely action.
Data Inputs
- Age (years)
- Resting Blood Pressure (mm Hg)
- Cholesterol (mg/dl)
- Maximum Heart Rate Achieved (bpm)
- Blood Sugar Level
- Symptoms: Headache, Body Pain, Tiredness
Machine Learning Prediction Logic (Simplified)
Instead of a complex trained model, this demo uses simple threshold rules based on common clinical risk factors:
- Higher age increases risk.
- High blood pressure & cholesterol increase risk.
- Low max heart rate achieved can be a risk indicator.
- Presence of symptoms like headache, body pain, tiredness adds to risk.
The tool categorizes risk into Low, Moderate, or High and advises immediate doctor consultation for moderate/high risks.
Check Your Heart Disease Risk
Find Nearby Hospitals
Please select your State and District/City to get hospital recommendations if you are at Moderate or High risk.