The work described in this paper builds upon our previous research on adoption modelling and aims to identify the best subset of features that could offer a better understanding of technology adoption. The current work is based on the analysis and fusion of two datasets that provide detailed information on background, psychosocial, and medical history of the subjects. In the process of modelling adoption, feature selection is carried out followed by empirical analysis to identify the best classification models. With a more detailed set of features including psychosocial and medical history information, the developed adoption model, using kNN algorithm, achieved a prediction accuracy of 99.41% when tested on 173 participants. The second-best algorithm built, using NN, achieved 94.08% accuracy. Both these results have improved accuracy in comparison to the best accuracy achieved (92.48%) in our previous work, based on psychosocial and self-reported health data for the same cohort. It has been found that psychosocial data is better than medical data for predicting technology adoption. However, for the best results, we should use a combination of psychosocial and medical data where it is preferable that the latter is provided from reliable medical sources, rather than self-reported.

本文中描述的工作建立在我们之前对采用建模的研究之上，旨在确定可以更好地理解技术采用的特征的最佳子集。目前的工作基于对两个数据集的分析和融合，这些数据集提供了有关受试者背景、心理社会和病史的详细信息。在建模采用过程中，先进行特征选择，然后进行实证分析，以确定最佳分类模型。具有更详细的特征集，包括社会心理和病史信息，开发的采用模型，使用kNN算法，在对173名参与者进行测试时，预测准确率达到了99.41%。使用 NN 构建的第二好的算法达到了 94.08% 的准确率。基于同一队列的心理社会和自我报告的健康数据，与我们之前的工作中达到的最佳准确度（92.48%）相比，这两个结果都提高了准确度。已经发现，心理社会数据比医学数据更能预测技术采用情况。然而，为了获得最佳结果，我们应该结合使用心理社会和医学数据，其中后者最好来自可靠的医学来源，而不是自我报告。