Alanazi H O, Abdullah A H, Qureshi K N, Ismail A S
Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia.
Department of Medical Science Technology, Faculty of Applied Medical Science, Majmaah University, Al Majmaah, Kingdom of Saudi Arabia.
Ir J Med Sci. 2018 May;187(2):501-513. doi: 10.1007/s11845-017-1655-3. Epub 2017 Jul 29.
Information and communication technologies (ICTs) have changed the trend into new integrated operations and methods in all fields of life. The health sector has also adopted new technologies to improve the systems and provide better services to customers. Predictive models in health care are also influenced from new technologies to predict the different disease outcomes. However, still, existing predictive models have suffered from some limitations in terms of predictive outcomes performance.
In order to improve predictive model performance, this paper proposed a predictive model by classifying the disease predictions into different categories. To achieve this model performance, this paper uses traumatic brain injury (TBI) datasets. TBI is one of the serious diseases worldwide and needs more attention due to its seriousness and serious impacts on human life.
The proposed predictive model improves the predictive performance of TBI. The TBI data set is developed and approved by neurologists to set its features. The experiment results show that the proposed model has achieved significant results including accuracy, sensitivity, and specificity.
信息通信技术（ICTs）已将生活各领域的趋势转变为新的综合运营和方法。卫生部门也采用了新技术来改进系统并为客户提供更好的服务。医疗保健中的预测模型也受到新技术的影响，以预测不同的疾病结果。然而，现有的预测模型在预测结果性能方面仍存在一些局限性。
为了提高预测模型的性能，本文通过将疾病预测分类为不同类别，提出了一种预测模型。为了实现该模型的性能，本文使用了创伤性脑损伤（TBI）数据集。TBI是全球范围内的严重疾病之一，由于其严重性和对人类生活的严重影响，需要更多关注。
所提出的预测模型提高了TBI的预测性能。TBI数据集由神经学家开发并批准以设置其特征。实验结果表明，所提出的模型取得了显著成果，包括准确性、敏感性和特异性。
