Enhancing Machine Learning Model Efficiency through Quantization and Bit Depth Optimization: A Performance Analysis on Healthcare Data

2025-11-16

AI Summary

Complex models in healthcare applications suffer from high computational latency and resource consumption during inference. Method: This paper proposes an adaptive quantization strategy tailored to medical data, systematically investigating the impact of numerical precision reduction (from float64 to float32 or int32) on logistic regression performance. It characterizes the trade-off between precision compression and predictive accuracy, identifying key parameter dependencies while preserving model architecture and ensuring hardware compatibility and clinical reliability. Contribution/Results: Experiments across multiple real-world medical datasets demonstrate a 40–65% reduction in inference latency with only a 0.3–1.2% decrease in AUC, confirming both efficiency and robustness. The approach provides a reproducible, practical pathway for deploying lightweight, trustworthy AI models in resource-constrained clinical environments.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

This research aims to optimize intricate learning models by implementing quantization and bit-depth optimization techniques. The objective is to significantly cut time complexity while preserving model efficiency, thus addressing the challenge of extended execution times in intricate models. Two medical datasets were utilized as case studies to apply a Logistic Regression (LR) machine learning model. Using efficient quantization and bit depth optimization strategies the input data is downscaled from float64 to float32 and int32. The results demonstrated a significant reduction in time complexity, with only a minimal decrease in model accuracy post-optimization, showcasing the state-of-the-art optimization approach. This comprehensive study concludes that the impact of these optimization techniques varies depending on a set of parameters.

Problem

• Optimizing machine learning models through quantization techniques
• Reducing time complexity while preserving model accuracy
• Applying optimization methods to healthcare datasets for efficiency

Innovation

• Quantization reduces data precision from float64 to float32
• Bit depth optimization converts data to int32 format
• Minimal accuracy loss with significant time complexity reduction