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In the era of healthcare revolution driven by Internet of Things (IoT) devices, the accurate classification and interpretation of patient health data are paramount. This paper introduces a novel approach titled “Design of an explainable AI Model with Q Convolutional Neural Networks (CNNs) for Patient Health Reporting.” The proposed model synergistically fuses Q Learning with CNNs, augmenting classification performance, and employs GridCAM++ to enhance explainability, thereby facilitating more precise health reporting for medical practitioners. The need for this work arises from the inherent complexities in handling patient health data collected through IoT devices. Existing methods often struggle to provide both accurate classifications and transparent insights, hindering effective decision-making by healthcare professionals. To address these limitations, this paper presents a pioneering solution that amalgamates key elements of reinforcement learning (Q Learning) with the discriminative power of CNNs. This fusion not only boosts classification accuracy but also brings interpretability to the forefront, allowing healthcare providers to comprehend the underlying reasoning behind AI-generated health reports. Characteristics of this work encompass the development of a hybrid Q Learning-CNN architecture tailored for healthcare applications. The Q Convolutional Neural Network (Q-CNN) leverages the intrinsic strengths of both reinforcement learning and deep convolutional networks. Furthermore, the integration of GridCAM++ augments the model’s interpretability by highlighting salient regions in input data that contribute to decision-making, offering unprecedented transparency in AI-driven health reporting process. This paper’s advantages are multifold. Firstly, the fusion of Q Learning with CNNs enhances the model’s classification performance, leading to more accurate and reliable patient health categorization. Secondly, the incorporation of GridCAM++ enables explainable AI (XAI) capabilities, providing healthcare practitioners with valued perceptions into the model’s decision-making process. This transparency not only builds trust in AI-assisted diagnostics but also aids doctors in making informed and timely clinical decisions. In summary, the presented work bridges the gap between accurate patient health reporting and interpretability by introducing a Q-CNN model enriched with GridCAM++. By doing so, it not only addresses the shortcomings of existing methods but also paves the way for a new era of healthcare, where AI augments the capabilities of medical professionals, ultimately leading to improved patient care and outcomes.

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Design of an Explainable AI Model with Q Convolutional Neural Networks for Patient Health Reporting

  • 会议论文

  • 发表日期：

    2025年

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  • Sivanagaraju Vallabhuni,
  • Kumar Debasis

摘要

In the era of healthcare revolution driven by Internet of Things (IoT) devices, the accurate classification and interpretation of patient health data are paramount. This paper introduces a novel approach titled “Design of an explainable AI Model with Q Convolutional Neural Networks (CNNs) for Patient Health Reporting.” The proposed model synergistically fuses Q Learning with CNNs, augmenting classification performance, and employs GridCAM++ to enhance explainability, thereby facilitating more precise health reporting for medical practitioners. The need for this work arises from the inherent complexities in handling patient health data collected through IoT devices. Existing methods often struggle to provide both accurate classifications and transparent insights, hindering effective decision-making by healthcare professionals. To address these limitations, this paper presents a pioneering solution that amalgamates key elements of reinforcement learning (Q Learning) with the discriminative power of CNNs. This fusion not only boosts classification accuracy but also brings interpretability to the forefront, allowing healthcare providers to comprehend the underlying reasoning behind AI-generated health reports. Characteristics of this work encompass the development of a hybrid Q Learning-CNN architecture tailored for healthcare applications. The Q Convolutional Neural Network (Q-CNN) leverages the intrinsic strengths of both reinforcement learning and deep convolutional networks. Furthermore, the integration of GridCAM++ augments the model’s interpretability by highlighting salient regions in input data that contribute to decision-making, offering unprecedented transparency in AI-driven health reporting process. This paper’s advantages are multifold. Firstly, the fusion of Q Learning with CNNs enhances the model’s classification performance, leading to more accurate and reliable patient health categorization. Secondly, the incorporation of GridCAM++ enables explainable AI (XAI) capabilities, providing healthcare practitioners with valued perceptions into the model’s decision-making process. This transparency not only builds trust in AI-assisted diagnostics but also aids doctors in making informed and timely clinical decisions. In summary, the presented work bridges the gap between accurate patient health reporting and interpretability by introducing a Q-CNN model enriched with GridCAM++. By doing so, it not only addresses the shortcomings of existing methods but also paves the way for a new era of healthcare, where AI augments the capabilities of medical professionals, ultimately leading to improved patient care and outcomes.