Department of Electrical and Computer Engineering (ECE), King Abdulaziz University, Jeddah, Saudi Arabia.
Center of Excellence in Intelligent Engineering Systems (CEIES), King Abdulaziz University, Jeddah, Saudi Arabia.
Comput Intell Neurosci. 2022 Aug 23;2022:7538643. doi: 10.1155/2022/7538643. eCollection 2022.
A combination of environmental conditions may cause skin illness everywhere on the earth, and it is one of the most dangerous diseases that can develop as a result. A major goal in the selection of characteristics is to produce predictions about skin disease instances in connection with influencing variables, which is one of the most important tasks. As a consequence of the widespread usage of sensors, the amount of data collected in the health industry is disproportionately large when compared to data collected in other sectors. In the past, researchers have used a variety of machine learning algorithms to determine the relationship between illnesses and other disorders. Forecasting is a procedure that involves many steps, the most important of which are the preprocessing of any scenario and the selection of forecasting features. A major disadvantage of doing business in the health industry is a lack of data availability, which is particularly problematic when data is provided in an unstructured format. Filling in missing numbers and converting between various types of data take somewhat more than 70% of the total time. When dealing with missing data in machine learning applications, the mean, average, and median, as well as the stand mechanism, may all be employed to solve the problem. Previous research has shown that the characteristics chosen for a model's overall performance may have an influence on the overall performance of the model's overall performance. One of the primary goals of this study is to develop an intelligent algorithm for identifying relevant traits in models while simultaneously eliminating nonsignificant attributes that have an impact on model performance. To present a full view of the data, artificial intelligence techniques such as SVM, decision tree, and logistic regression models were used in conjunction with three separate feature combination methodologies, each of which was developed independently. As a consequence of this, their accuracy, -measure, and precision are all raised by a factor of ten, respectively. We then have a list of the most important features, together with the weights that have been allocated to each of them.
环境条件的综合作用可能导致地球上任何地方的皮肤疾病，而且这是最危险的疾病之一，可能会因此而发展。在选择特征时，一个主要目标是根据影响变量对皮肤疾病病例做出预测，这是最重要的任务之一。由于传感器的广泛使用，与其他部门相比，医疗行业收集的数据量不成比例地增加。过去，研究人员使用了多种机器学习算法来确定疾病与其他疾病之间的关系。预测是一个涉及多个步骤的过程，其中最重要的是对任何场景进行预处理和选择预测特征。在医疗行业开展业务的一个主要缺点是数据可用性不足，当数据以非结构化格式提供时，这尤其成问题。填补缺失数字和在各种类型的数据之间转换需要花费超过总时间的 70%。在处理机器学习应用程序中的缺失数据时，可以使用平均值、平均值、中位数以及 stand 机制来解决问题。先前的研究表明，为模型的整体性能选择的特征可能会对模型的整体性能产生影响。本研究的主要目标之一是开发一种智能算法，用于识别模型中相关特征，同时消除对模型性能有影响的非显著属性。为了全面展示数据，使用了支持向量机、决策树和逻辑回归模型等人工智能技术，并结合了三种独立开发的特征组合方法。因此，它们的准确性、-度量和精度分别提高了十倍。然后，我们列出了最重要的特征及其分配给它们的权重。
