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<p>Poly-cystic ovary syndrome (PCOS) is a prevalent condition that impinges women in their prime reproductive years. Its primary trait is the elevated levels of androgen, a male hormone in female body and leads to several symptoms, including ovarian cysts, irregular menstruation cycles, infertility, obesity, and excessive hair development. Since PCOS exact cause is unknown and its symptoms are uncertain, a timely and accurate diagnosis is essential. In these situations, a (ML) Machine Learning-based PCOS prediction model aids in the diagnostic procedure, addresses time constraints and potential inaccuracies. A plethora of feature selection techniques have been built to discover the best optimal features for the classification of medical dataset. However, finding an efficient solution is still difficult due to noise and redundant information which may degrade the model performance. In this article, a hybrid filter-wrapper approach is proposed to identify the optimal attributes. An ensemble filter method is built, the union of top k attributes from individual filters is considered for further process. Then, Reinforcement Learning-based Binary Equilibrium Optimizer is used to find the reduced optimal features. Here, RL uses SARSA (State–Action–Reward–State–Action) to increase the population diversity in the search space, to avoid the problem of local optima, finally balances the exploration capability during the search. Then, identified optimal features are given as input to BMFK (Bonferroni Mean Fuzzy KNN) classifier and achieved 96.32% of accuracy. Further, several machine learning classifiers have been employed for the prompt diagnosis of PCOS. Finally, Random Forest has produced the highest accuracy of 95.62% than other models.</p>

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Comprehensive Feature-Driven PCOS Predictor: A Reinforcement Learning-Based Binary Equilibrium Optimization Approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月22日

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  • S. Reka,
  • T. Suriya Praba,
  • Krishna Kumar Manchala,
  • Anna Venkateswarlu

摘要

Poly-cystic ovary syndrome (PCOS) is a prevalent condition that impinges women in their prime reproductive years. Its primary trait is the elevated levels of androgen, a male hormone in female body and leads to several symptoms, including ovarian cysts, irregular menstruation cycles, infertility, obesity, and excessive hair development. Since PCOS exact cause is unknown and its symptoms are uncertain, a timely and accurate diagnosis is essential. In these situations, a (ML) Machine Learning-based PCOS prediction model aids in the diagnostic procedure, addresses time constraints and potential inaccuracies. A plethora of feature selection techniques have been built to discover the best optimal features for the classification of medical dataset. However, finding an efficient solution is still difficult due to noise and redundant information which may degrade the model performance. In this article, a hybrid filter-wrapper approach is proposed to identify the optimal attributes. An ensemble filter method is built, the union of top k attributes from individual filters is considered for further process. Then, Reinforcement Learning-based Binary Equilibrium Optimizer is used to find the reduced optimal features. Here, RL uses SARSA (State–Action–Reward–State–Action) to increase the population diversity in the search space, to avoid the problem of local optima, finally balances the exploration capability during the search. Then, identified optimal features are given as input to BMFK (Bonferroni Mean Fuzzy KNN) classifier and achieved 96.32% of accuracy. Further, several machine learning classifiers have been employed for the prompt diagnosis of PCOS. Finally, Random Forest has produced the highest accuracy of 95.62% than other models.