Skip to main content
SimilarExport ZIP
100%

heart data parallel coordinate plot

✓ Published0🌍 Public
AAishwarya Manjunath
Last edited Apr 29, 2022
Created on Apr 29, 2022

This visualization presents a parallel coordinate plot of heart patient data, showing relationships between RestingBP, Cholesterol, and MaxHR. Each line represents a patient, colored by chest pain type (ATA, NAP, ASY, TA) using a d3.scaleOrdinal color scale. Users can hover over lines to highlight a specific chest pain category, and drag the axes to reorder dimensions. The implementation uses d3.csv for data loading, SVG paths for rendering lines, d3.axisLeft for axes, and d3.drag to enable interactive axis repositioning.

AI-generated description
MIT Licensed

Similar vizzes

Loading thumbnail…

Fork of Iris parallel coordinate plot

This interactive parallel coordinate plot explores the relationship between RestingBP, Cholesterol, and MaxHR in a heart disease dataset. Each line represents a patient, colored by their ChestPainType (ATA, NAP, ASY, TA), with several data points excluded. Hovering a line highlights all patients of that type while dimming others. Users can click and drag the vertical axes to reorder dimensions, with the line paths updating dynamically. The visualization is built with D3 v4, using `d3.scaleOrdinal` for colors, `d3.scaleLinear` for each axis, `d3.line()` for paths, and `d3.drag()` for interactive axis manipulation, rendered as SVG.

AAishwarya Manjunath
89% match
Loading thumbnail…

Parallel Coordinates with Brushing (Heart_Failure_Prediction)

This parallel coordinates visualization uses D3.js to explore heart disease data, mapping clinical variables like age, cholesterol, resting blood pressure, and maximum heart rate across categorical dimensions. Each polyline represents a patient, colored by heart disease status, with brushing enabled on each axis to filter the dataset interactively. Brushed intervals constrain the data across all selected dimensions simultaneously, and the filtered subset is recomputed with memoized efficiency, updating the rendering in real time. The plot includes categorical and quantitative axes, a legend, and animated transitions.

SSree Likhith
79% match
Loading thumbnail…

Visualizing correlations between heart disease patients' data

This interactive scatterplot displays correlations between heart disease patient attributes—such as age, cholesterol, and BMI—with heart disease status, using a red dot for each individual. Users select any attribute pair from the two dropdown menus to swap the axes, and the points animate smoothly into their new positions. The visualization is built with D3 v7, using the `scaleLinear`, `extent`, `axisLeft`, and `axisBottom` APIs for scaling and axes, while `transition.attrTween` or `transition().duration().delay()` handles the animation. The data comes from a CSV file loaded via `d3.csv`.

AAshtone Onyango
78% match
Loading thumbnail…

Visualizing correlations between heart disease patients' data

This interactive scatter plot lets users explore correlations among heart disease patients by selecting any two metrics from dropdown menus, which map to the x- and y-axes. Each point represents a patient, colored by heart disease status, from a CSV dataset. Built with D3 v7, it uses scaleLinear, axisBottom, axisLeft, and transition().delay().duration() for animated point placement. The chart renders in SVG with adjustable margins and axis labels.

AAshtone Onyango
78% match
Loading thumbnail…

Fork of Fork of Parallel Coordinate Plot

This visualization uses a parallel coordinate plot to display the Iris Dataset, showing how three numeric attributes (RestingBP, Cholesterol, MaxHR) vary across different classes. Each polyline represents a single data point, colored by its categorical class using a custom color mapping. The code employs d3.csv to load the data, then uses d3.scalePoint, d3.scaleLinear, and d3.line to construct the axes and paths, with d3.axisLeft for tick marks. The SVG rendering includes a manually positioned legend and title, while d3.select and d3.append manage the DOM structure.

AAishwarya Manjunath
76% match
Loading thumbnail…

Data set on Heart attack possibility

This visualization plots serum cholesterol against resting blood pressure, with each point representing a patient from the Heart Disease UCI dataset. Circle size encodes age, while color distinguishes gender, and tooltips reveal heart attack risk classification. Built with Vega-Lite and D3.js, the scatterplot uses quantitative scales for both axes and nominal color encoding for sex, drawing data from a CSV fetched via d3.csv.

AAbdul Salam Almadani
75% match
Loading thumbnail…

Fork of Data set on Heart attack possibility

This visualization plots the relationship between cholesterol and resting blood pressure, with each point representing a patient from the Heart Disease UCI dataset. The size of each point encodes the patient's age, while color distinguishes gender (blue for male, red for female). The visualization uses the Vega-Lite API via the `vl` library, with data loaded from a CSV file. This view allows exploration of how these cardiovascular risk factors correlate with the likelihood of heart attack, as indicated in the tooltip showing the target attribute.

Kkshethia12
75% match
Loading thumbnail…

Fork of Scatter Plot with Menus - Heart Disease Dataset

This scatter plot visualizes heart disease patient data from the UCI dataset, mapping age against maximum heart rate achieved. Users can dynamically change both axes via dropdown menus, selecting from quantitative variables like resting blood pressure or categorical options like patient sex. Built with D3 v6, the visualization uses `scaleLinear` and `scalePoint` for axis scaling, with smooth transitions animating circle positions and radii. The plot renders as SVG, with circles colored by patient sex (red for female, blue for male) and axis updates triggered through custom menu components dispatching change events.

RRyanYun0017
75% match