Abstract :

All Spheres Of The Healthcare Sector, Finances, And Social Media Are Rapidly Growing In Terms Of High-dimensional Data And Require Robust Schemes Of Useful Features Extraction And Effective Predictors Modeling. The Research Article Focuses On A Data-driven Artificial Intelligence (AI) Model That Is Capable Of Dealing With High-dimensional Feature Space And Improve Interpretability Of A Model And Prediction Accuracy. The Architecture Is A Combination Of State-of-art Methods Of Dimensionality Reduction, Features Selection Algorithms, And Deep Learning Architecture That Learns Non-linear And Complicated Relationships In Big Data. Broad Based Experimentations With Benchmark Data Show That, The Suggested Framework Outdoes The Traditional Frameworks In Terms Of Predictive Accuracy, Processing Speed And Scalability. The Framework Also Provides Information About The Contribution Brought By Each Specific Feature To Make Decision Easier And Have A Conception Of Its Territory. The High Dimensional Analysis Can Also Develop The AI Derived In This Work Offering The Realistic Approach To Be Applied In The Actual Applicability Of Prediction Modeling And Revealing Knowledge In The Real Life. Keywords: High-dimensional Data, Feature Learning, Predictive Modeling, Artificial Intelligence, Dimensionality Reduction, Deep Learning, Feature Selection

Published:

20-12-2025

Issue:

Vol. 25 No. 12 (2025)

Page Nos:

405-413

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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