LuminaAI/Heart_Disease-Tabular

Heart Disease Tabular Data RCL Dataset

Overview

This dataset contains tabular data explicitly structured for heart disease classification tasks using Lumina AI's Random Contrast Learning (RCL) algorithm via the PrismRCL application. Tabular datasets comprise numeric or categorical data organized into individual .txt files with space-separated values.

Dataset Structure

The dataset structure for heart disease classification training:

heart-disease_data/
    train/
        [class_1]/
            sample_001.txt
            sample_002.txt
            ...
        [class_2]/
            sample_001.txt
            sample_002.txt
            ...
    test/
        [class_1]/
            sample_001.txt
            sample_002.txt
            ...
        [class_2]/
            sample_001.txt
            sample_002.txt
            ...

• Classes: Folder names represent distinct categories of heart disease presence or absence.
• Tabular Samples: Each .txt file represents a single data sample with features provided as space-separated values.

Tabular Data Preparation

PrismRCL requires the following specific preparation for tabular datasets:

• Data samples must be in .txt format.
• Each file should contain a single line of space-separated features.
• Normalization of numerical values is not required when using PrismRCL version 2.4.0 or later.
• All file names must be unique across class folders.

Usage (Tabular-specific)

Use PrismRCL for training with heart disease tabular data:

C:\PrismRCL\PrismRCL.exe naivebayes rclticks=10 ^
  data=C:\path\to\heart-disease_data\train testdata=C:\path\to\heart-disease_data\test ^
  savemodel=C:\path\to\models\heart_disease_model.classify ^
  log=C:\path\to\log_files stopwhendone

Explanation of Command

• naivebayes: Specifies Naive Bayes as the evaluation method for tabular classification.
• rclticks: Number of RCL iterations during training.
• data & testdata: Paths to training and testing tabular datasets.
• savemodel: Output path for the trained classification model.
• log: Directory for storing log files.
• stopwhendone: Automatically terminates the session after training completion.

Auto Optimize

PrismRCL offers an Auto Optimize feature to automatically identify optimal training parameters for your dataset. This eliminates manual parameter tuning by systematically evaluating your data to determine the best settings for parameters such as evaluation method, rclticks, and boxdown. Execute the following command:

C:\PrismRCL\PrismRCL.exe auto-optimize data=C:\path\to\heart-disease_data\train log=C:\path\to\log_files

Optimized parameters are stored in a summary file within your specified log directory.

License

This dataset is licensed under the MIT License.

Original Source

Prepared explicitly by Lumina AI for RCL-based tabular classification training. Please credit Lumina AI when using this dataset in research or applications.

Additional Information

Refer to the PrismRCL Technical Documentation v2.6.2 for more detailed guidance on tabular data preparation and parameter specifications.