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<p>The rapid advancements in artificial intelligence (AI) have revolutionized smart healthcare, driving innovations in wearable technologies, continuous monitoring devices, and intelligent diagnostic systems. However, security, explainability, robustness, and performance optimization challenges remain critical barriers to widespread adoption in clinical environments. This research presents an innovative algorithmic method using the proposed EasyDiagnos algorithm to improve feature selection in healthcare datasets and overcome problems. EasyDiagnos integrating genetic algorithms (GA), explainable artificial intelligence (XAI), and Permutation and Combination Techniques (PCT), the algorithm optimizes Clinical Decision Support Systems (CDSS), thereby enhancing predictive accuracy and interpretability. The proposed method is validated across three diverse healthcare datasets using six distinct machine learning algorithms, demonstrating its robustness and advantage over conventional feature selection techniques. The results underscore the incremental improvement of EasyDiagnos in smart healthcare, enabling personalized and transparent patient care. EasyDiagnos, when combined with MLP, achieved a maximum accuracy of 98.79% on the lung cancer dataset, highlighting its capability to improve clinical decision-making processes in real-world healthcare applications.</p>

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EasyDiagnos: An Accurate Feature Selection Framework for Automated Diagnosis in Smart Healthcare

  • 期刊论文

  • 发表日期：

    2025年8月3日

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  • Prasenjit Maji,
  • Amit Kumar Mondal,
  • Hemanta Kumar Mondal,
  • Saraju P. Mohanty

摘要

The rapid advancements in artificial intelligence (AI) have revolutionized smart healthcare, driving innovations in wearable technologies, continuous monitoring devices, and intelligent diagnostic systems. However, security, explainability, robustness, and performance optimization challenges remain critical barriers to widespread adoption in clinical environments. This research presents an innovative algorithmic method using the proposed EasyDiagnos algorithm to improve feature selection in healthcare datasets and overcome problems. EasyDiagnos integrating genetic algorithms (GA), explainable artificial intelligence (XAI), and Permutation and Combination Techniques (PCT), the algorithm optimizes Clinical Decision Support Systems (CDSS), thereby enhancing predictive accuracy and interpretability. The proposed method is validated across three diverse healthcare datasets using six distinct machine learning algorithms, demonstrating its robustness and advantage over conventional feature selection techniques. The results underscore the incremental improvement of EasyDiagnos in smart healthcare, enabling personalized and transparent patient care. EasyDiagnos, when combined with MLP, achieved a maximum accuracy of 98.79% on the lung cancer dataset, highlighting its capability to improve clinical decision-making processes in real-world healthcare applications.