Machine learning is now widely used in various fields, and it has made a big splash in the field of disease diagnosis. But traditional machine learning models are general-purpose, that is, one model is used to evaluate the health status of different patients. A general-purpose machine learning algorithm depends on a large amount of data and requires abundant computing power support, relies on the average level to describe the model performance, and cannot achieve optimal results on a specific problem. In this paper, we propose to train a unique model for each patient to improve the accuracy and ease of use of the model. The proposed approach to solving a problem in the paper is from three perspectives (1) targeted data processing, (2) model structure design: Passing in patient-related information into the model, and (3) hyperparameter tailored optimization. The preliminary experimental results show that using the custom model has advantages of high accuracy, high confidence, and low resource required to diagnose a patient. In the Hepatitis C dataset, over 99% accuracy and 94% recall were achieved using a smaller dataset (only 615 individuals' data) without knowledge of the relevant field. Traditional algorithms such as XGBoost or multi-algorithm ensemble could achieve less than 95% accuracy and only less than 70% recall. Out of a total of 56 patients, the custom model was able to identify 53 patients 20 more than traditional methods, bringing a new and efficient tool for future hepatitis C prevention and treatment efforts. INDEX TERMS Machine learning, custom model, hepatitis C, disease diagnosis, data augmentation, parameter optimization.
Concave surfaces are widely used in the shells of smart devices, such as smartphones, watches, or molds. The quality of the concave surface is important in enhancing the value of these products. In order to improve the surface quality, the polishing process is crucial for removing defects on the concave surface and for smoothing the surface after machining or grinding. Magnetic assisted polishing is a promising method that can be used to meet the high standard of surface quality required. In this work, as a promising smart material in nano-precision polishing, magnetic compound fluid (MCF) slurry was used for the first time to polish a concave surface with a magnet that is magnetized in the radial direction. A simulation of the magnetic field distribution was performed in advance to clarify the polishing characteristics in theory. Subsequently, a polishing experiment was conducted to investigate the feasibility of this polishing method. Finally, the results demonstrated that both a curved surface and a flat surface could be polished successfully. Furthermore, the nano-precision PV value (the distance from the peak to the valley in the surface profile) and the surface roughness Ra were obtained for both areas, and this method was demonstrated to be capable of polishing concave surfaces and worthy of further research.
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