Context. The task of automation of feature informativeness estimation process in diagnostics and pattern recognition problems is solved. The object of the research is the process of informative feature selection. The subject of the research are the criteria of feature informativeness estimation. Objective. The research objective is to develop the system of criteria for feature informativeness estimation which enables to compute informativeness of interdependent feature sets. Method. The system of criteria for feature informativeness estimation is proposed. The proposed system is based on the idea that feature significance is computed according to spatial location of observations of different classes (size of changing of output parameter). The developed criteria system enables to estimate individual and group feature informativeness in classification and regression problems in situations when initial data samples contain redundant and interdependent features as well as observations with missing values. The proposed criteria don't require to construct models based on the estimated feature combinations, in such a way considerably reducing time and computing costs for informative feature selection. Application of the proposed criteria for estimation and selection of informative features allows to reduce structural complexity of synthesized diagnosis and recognition models, to raise its interpretability and generalization ability due to removing of insignificant, interdependent and redundant features in diagnostics and pattern recognition problems. Results. The software which implements the proposed system of criteria for feature informativeness estimation and allows to select informative features for synthesis of recognition models based on the given data samples has been developed. Conclusions. The conducted experiments have confirmed operability of the proposed system of criteria for feature informativeness estimation and allow to recommend it for processing of data sets for pattern recognition in practice. The prospects for further researches may include the modification of the known feature selection methods and the development of new ones based on the proposed system of criteria for individual and group feature informativeness estimation.
Context. The problem of skin disease diagnosis was investigated in the paper. Its actuality is caused by the necessity of automation of at least advisory medical decision making. Such decisions are made in telemedicine, for instance, when skin disease diagnostics is performed under specific conditions. These conditions are specified by situations when data for analysis are collected but a qualified doctor has no possibility to process the data and to make a diagnosis decision based on it. The object of the study is a process of skin disease diagnosis. Objective. The objective of the study is to develop a skin disease diagnosis method to automate making of advisory medical diagnosis decisions and to increase efficiency of such decisions. Method. The skin disease diagnosis method was proposed in the work. This method applies the modified ResNet50 model. It was proposed to add layers to the ResNet50 model and to train it using transfer learning and fine-tuning techniques. The method also defines image processing in particular through the change of its resolution and uses oversampling technique to prepare a dataset for model training. Results. Experimental investigation of the proposed method was performed using the HAM10000 dataset which contains images of skin diseases. The images were collected using dermatoscopy method. The dataset contains observations for 7 different skin diseases. The proposed method is characterized by the accuracy of 96.31% on this dataset. It is improved accuracy in comparison with the existing neural network models. Software component model was created to give a possibility to integrate the proposed method into a medical diagnosis system. Conclusions. The obtained results of the investigation suggest application of the proposed skin disease method in medical diagnostic system to make advisory decisions by the system and to support making final decisions by a doctor.
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