Heart Disease Prediction Dashboard

Published November 24, 2025

Software Developer Janaka Alwis

Introduction

The Heart Disease Prediction Dashboard is a sophisticated interactive tool designed to analyze and visualize the critical factors influencing heart attack risk. Leveraging Dashtera’s powerful no-code platform, this dashboard transforms a comprehensive heart disease dataset into actionable insights through dynamic charts and intuitive exploration.

This dashboard aims to help healthcare professionals, researchers, and data scientists to:

Visualize relationships between medical and demographic factors and heart disease risk,
Compare high-risk versus low-risk patient groups, and
Support predictive modeling and clinical decision-making efficiently.

The dashboard utilizes a merged heart disease dataset with 1,888 patient records, integrating multiple publicly available sources for robust analysis.

Dataset

The Heart Disease Prediction Dataset combines five publicly available datasets, totaling 1,888 records and 14 features critical for heart attack risk prediction. The data was cleaned to remove missing or inconsistent records, ensuring high integrity for machine learning and analytical applications.

Variable	Description	Value/Units
age	Age of the patient	Numeric (years)
sex	Gender of the patient	1 = male, 0 = female
cp	Chest pain type	0 = Typical angina, 1 = Atypical angina, 2 = Non-anginal pain, 3 = Asymptomatic
trestbps	Resting Blood Pressure	mm Hg
chol	Serum Cholesterol	mg/dl
fbs	Fasting blood sugar > 120 mg/dl	1 = true, 0 = false
restecg	Resting electrocardiographic results	0 = Normal, 1 = ST-T abnormality, 2 = Left ventricular hypertrophy
thalach	Maximum heart rate achieved	Numeric
exang	Exercise-induced angina	1 = yes, 0 = no
oldpeak	ST depression induced by exercise	Numeric
slope	Slope of the peak exercise ST segment	0 = Upsloping, 1 = Flat, 2 = Downsloping
ca	Number of major vessels colored by fluoroscopy	0-3
thal	Thalassemia type	1 = Normal, 2 = Fixed defect, 3 = Reversible defect
target	Heart attack risk	1 = More risk, 0 = Less risk

The following table outlines the original sources that contributed to the combined dataset.

Dataset	Records	Reference
Heart Attack Analysis & Prediction Dataset	304	Rahman, 2021
Heart Disease Dataset	1,026	Lapp, 2019
Heart Attack Prediction (Dataset 3)	295	Damarla, 2020
Heart Attack Prediction (Dataset 4)	271	Anand, 2018
Heart CSV Dataset	290	Nandal, 2022

About Dashtera

What is Dashtera?

Dashtera is a cloud-based, no-code dashboard platform that enables users to connect diverse data sources and build interactive dashboards without any coding. It offers drag-and-drop tools, dynamic filters, and advanced visualization options that suit both beginners and experts.

Key Features

Connects to various data sources (CSV, Excel, APIs, etc.)
Wide range of chart types, including advanced statistical visuals
Interactive drill-downs and dynamic filters
Shareable dashboards with flexible layouts
Supports calculated fields and transformations
User-friendly drag-and-drop interface

Advantages Over Similar Tools

Extremely easy to use—minimal technical expertise required
Rapid dashboard creation and deployment
Suitable for both beginners and advanced users
Lightweight yet powerful compared to Tableau or Power BI

Dashboards

Patient Demographic Factors

This dashboard provides detailed demographic insights into how age and sex correlate with heart attack risk.

The visualization includes:

Patient Sex Group Pie Chart
Displays Male (1300 patients) vs. Female (588 patients) with drill-down capability by risk level and age group.
Patient Risk Level Donut Chart
Shows High Risk (977 patients) vs. Less Risk (911 patients).
Patient Age Group Pie Chart
Segments patients into age groups: Young (29–39), Early Middle (40–49), Late Middle (50–59), and Senior (60–77).
Age Group Risk Stacked Horizontal Bar Chart
Displays counts of high and less risk patients across age groups, segmented by sex.
Age Group Risk Grouped Bar Chart
Patient Age Distribution Box Plot
Shows age quartiles for less risk (51 to 62 years) and high risk (45 to 65 years) patients.
Patient Age Histograms and Distribution Charts
Separate histograms for all patients, males, and females.

The male gender make up the majority of the dataset, with notable differences in risk levels across age and sex groups. Late middle-aged males appear heavily represented in both risk categories, while the influence of age on risk varies distinctly between men and women.

Serum Cholesterol with Gender, Age, and Risk

This dashboard explores serum cholesterol levels and their relation to heart attack risk, differentiated by gender and age.

The visualization includes:

Male Average Cholesterol Step Area Chart (Low vs. High Risk)
Female Average Cholesterol Step Area Chart (Low vs. High Risk)
Total Average Cholesterol Step Area Chart (Male vs. Female)
Average Cholesterol vs. Age Regression Charts (Total, Male, Female)

Higher cholesterol correlates with increased heart attack risk, with notable gender differences revealed by regression trends across age groups.

Resting ECG Results and Chest Pain Types

This dashboard focuses on how resting ECG results and chest pain types relate to heart attack risk.

The visualization Includes:

Resting ECG Risk Level Stacked Bar Chart and Spider Chart
Chest Pain Types Risk Level Stacked Bar Chart and Spider Chart

ECG Results Summary:

ECG Result	High Risk Count	Low Risk Count	High Risk %	Low Risk %
Normal	397	521	40.63%	57.19%
ST-T Abnormality	502	310	51.38%	34.03%
LV Hypertrophy	78	80	7.98%	8.78%

Chest Pain Types Summary:

Chest Pain Type	High Risk Count	Low Risk Count	High Risk %	Low Risk %
Typical Angina	200	566	20.47%	62.13%
Atypical Angina	221	70	22.62%	7.68%
Non-anginal Pain	364	135	37.26%	14.82%
Asymptomatic	100	102	10.24%	11.20%

ST-T abnormalities and non-anginal chest pain are strongly linked with higher risk, whereas normal ECG and typical angina are more common in lower risk patients.

Other Clinical Factors and Risk Relationship

This dashboard examines additional clinical factors and their relationship with heart attack risk.

The visualization includes:

Resting Blood Pressure Box Plot
Maximum Heart Rate Achieved Box Plot
Serum Cholesterol Box Plot
ST Depression Induced by Exercise Box Plot
Fasting Blood Sugar >120 mg/dl Grouped Bar Chart
Exercise-Induced Angina Grouped Bar Chart

Fasting Blood Sugar >120 mg/dl results:

Blood Sugar Level	Less Risk	More Risk
False	765	843
True	146	134

Exercise-Induced Angina results:

Exercise Angina	Less Risk	More Risk
No	469	793
Yes	442	184

Elevated fasting blood sugar and exercise-induced angina levels differ significantly between risk groups. Other vital signs such as blood pressure, cholesterol, heart rate, and ST depression also vary noticeably with risk.

Conclusion

The Heart Disease Prediction Dashboard built with Dashtera provides a comprehensive and interactive platform for analyzing key factors influencing heart attack risk. By using MySQL queries to summarize and aggregate the data, the dashboard ensures that complex information is efficiently processed and organized before visualization. This approach allows for precise and meaningful insights while maintaining data accuracy and integrity.

Leveraging Dashtera’s no-code environment, the dashboard transforms this summarized data into dynamic, easy-to-explore visualizations that users can interact with without needing technical expertise. This combination of powerful data querying and user-friendly visualization empowers healthcare professionals, researchers, and data scientists to identify high-risk patients, understand complex relationships, and enhance clinical decision-making and preventive care strategies.

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