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Cataract is a lens opacification caused by protein denaturation which leads to a decrease in vision and even results in complete blindness at later stages. The concept of a classification system of automatic cataract detection based on retinal fundus images has been proposed in previous research work which consists of fundus image preprocessing, feature extraction and the building of classifier. This paper proposes to make use of the method of PCA (principal component analysis) to reduce the dimensionality of two sets of features extracted from fundus images which are wavelet features and sketch features, respectively. We find the classification accuracy rate based on new features after PCA transformation is nearly the same with the original ones, but the computation cost can really be decreased a lot. Experiment results provide a bright foresight in later practical application of classification system of automatic cataract detection.

In order to reduce the importance of neighboring rough set only by single attribute, this paper proposes an improved neighborhood rough set attribute reduction algorithm (INRS), which increases the dependence of conditional attributes based on considering the importance of individual features. Relationship, that is, whether it affects the effect of other conditional attributes on decision attributes after deleting the attribute. The importance of a certain attribute to the decision attribute is divided into two parts: direct influence (after the attribute is deleted, the obtained decision attribute depends on the degree of reduction of the conditional attribute) and indirect influence (relative to the influence of other conditional attributes on the role of the decision attribute when there is no such attribute), so that the importance of each attribute can be clearly identified. When the attribute is reduced, the potential attributes are not reduced. In this paper, the data of diabetes in a hospital of the National Population and Health Science Data Sharing Service Platform was collected. The attribute reduction algorithm was used to attribute the diabetes dataset, and the random forest (RF) was used for classification prediction, which formed high precision. The Diabetes Prediction Model aims to provide support for doctors’ clinical diagnosis and disease research to improve the level of clinical diagnosis and treatment.

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