During the COVID-19 pandemic time, educational institutions have really played a good role in imparting online education to students. Their career and academic tenure were not affected as contrary to the past pandemics throughout world history. All this has been possible through long sessions of classes, quizzes, assignments, discussions, chat interactions, and examinations through online video-based learning using computer interactive measures. The students were privileged to utilize digital technologies for longer durations for learning purposes. However, these long stretches have adversely affected their body postures, and physical and mental health as they majorly remain confined to chairs with restricted levels of physical activities. Thus, there is a need to have a model which can act as an insight for parents, doctors (pediatricians), and academic policymakers to decide on maximum hours for online teaching and related activities during future pandemics. The novel model proposed in this work helps to predict the impact of enhanced students’ computer interactions on their physical and mental health. The method proposed uses a novel model which is advanced and computationally strong. The model follows a two-step methodology, where at the first level, a variant of already existing machine learning algorithm is proposed and at the next level, it is optimized further using a hybrid bio-inspired optimization algorithm. The model consists of proposing a variant of XGBoost model (step1 optimization) followed by a hybrid bio-inspired algorithm (step2 optimization). The work considers a humongous dataset with varied age groups of students with more than 10 attributes. The proposed model is highly efficient in making predictions with 98.07% accuracy level and 98.43% F1-score. The time complexity of the model obtained is also of order of “n” where “n” depicts the number of input variables. Strong empirical results for other parameters also like specificity (95.63%) and sensitivity (96.74%) ascertain the enhanced predictive power generated using the proposed model. An extensive comparative study with other machine learning models ascertains the elevated accuracy and predictive power using the proposed model. Till now none of the researchers have proposed any such pioneering tool for parents, doctors, and academicians using advanced machine learning algorithms.

在COVID-19大流行期间，教育机构在向学生提供在线教育方面确实发挥了良好的作用。与世界历史上过去的流行病相反，他们的职业生涯和学术任期没有受到影响。所有这一切都是通过长时间的课程、测验、作业、讨论、聊天互动以及使用计算机交互措施的在线视频学习进行的考试而实现的。学生们有幸能够更长时间地利用数字技术进行学习。然而，这些长时间的伸展运动对他们的身体姿势以及身心健康产生了不利影响，因为他们主要仍然局限于椅子上，身体活动水平受到限制。因此，需要有一个模型可以为父母、医生（儿科医生）、和学术政策制定者决定未来大流行期间在线教学和相关活动的最长时数。这项工作中提出的新颖模型有助于预测增强的学生计算机交互对其身心健康的影响。所提出的方法使用了一种先进且计算能力强的新颖模型。该模型遵循两步方法，在第一级，提出现有机器学习算法的变体，在下一个级，使用混合仿生优化算法进一步优化。该模型包括提出 XGBoost 模型的变体（步骤 1 优化）和混合仿生算法（步骤 2 优化）。这项工作考虑了一个庞大的数据集，其中包含不同年龄组的学生，具有 10 多个属性。所提出的模型在预测方面非常高效，准确率达到 98.07%，F1 分数达到 98.43%。获得的模型的时间复杂度也是“n”的量级，其中“n”表示输入变量的数量。其他参数（如特异性（95.63%）和敏感性（96.74%））的强有力的实证结果确定了使用所提出的模型产生的增强的预测能力。与其他机器学习模型的广泛比较研究确定了使用所提出的模型的准确性和预测能力的提高。到目前为止，还没有研究人员使用先进的机器学习算法为父母、医生和院士提出任何此类开创性工具。获得的模型的时间复杂度也是“n”的量级，其中“n”表示输入变量的数量。其他参数（如特异性（95.63%）和敏感性（96.74%））的强有力的实证结果确定了使用所提出的模型产生的增强的预测能力。与其他机器学习模型的广泛比较研究确定了使用所提出的模型的准确性和预测能力的提高。到目前为止，还没有研究人员使用先进的机器学习算法为父母、医生和院士提出任何此类开创性工具。获得的模型的时间复杂度也是“n”的量级，其中“n”表示输入变量的数量。其他参数（如特异性（95.63%）和敏感性（96.74%））的强有力的实证结果确定了使用所提出的模型产生的增强的预测能力。与其他机器学习模型的广泛比较研究确定了使用所提出的模型的准确性和预测能力的提高。到目前为止，还没有研究人员使用先进的机器学习算法为父母、医生和院士提出任何此类开创性工具。与其他机器学习模型的广泛比较研究确定了使用所提出的模型的准确性和预测能力的提高。到目前为止，还没有研究人员使用先进的机器学习算法为父母、医生和院士提出任何此类开创性工具。与其他机器学习模型的广泛比较研究确定了使用所提出的模型的准确性和预测能力的提高。到目前为止，还没有研究人员使用先进的机器学习算法为父母、医生和院士提出任何此类开创性工具。