This research paper proposes the findings of the accuracy of the result by using the K-Means clustering technique in prediction of heart disease diagnosis with real and artificial datasets. K-Means Clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. Each cluster is assigned a random target number of clusters-k and started from a random initialization. The proposed technique classifies the group of the objects based on attributes into K number of groups. The grouping is done by minimizing the sum of squares of distances between data using Euclidean distance formula and the corresponding cluster centroid. The research result shows that the integration of clustering gives promising results with highest accuracy rate and robustness.

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Glaucoma disease detection from retinal images using classifiers like least square -Support Vector Machine classifier, random forest, dual Sequential Minimal Optimization classifier, naive bayes classifier and artificial neural networks. The textual features obtained from retinal images are used for this classification. Energy distributions over wavelet sub bands provides these features. The proposed system is using discrete wavelet transform to extract different wavelet features obtained from the three filters symlets (sym3), daubechies (db3)and biorthogonal (bio3.3, bio3.5, and bio3.7) wavelet filters. The energy signatures obtained from 2-D discrete wavelet transform is used for classifying and detecting glaucomatous and normal retinal images.

The high death rates are occurred due to the Melanoma among the skin tumor persons. Melanoma is more dangerous when it raises inside of the skin layer. Hence, watch the wound in depth of the skin is a significant cause to identify melanoma. A (NI) non-invasive computerized dermoscopic (DS) method is introduced in these study. Existing DS system many faces various challenges includes segmentation and classification for detecting the skin cancer. The objective of the research work to improve the segmentation and classification performance. In DS images hair removal and segmentation are performed by using Hybrid Laplacian of Gaussian (HLOG) filter and Flexible Kernel-Based Fuzzy Means (FKFCM), whereas Patch-Local binary patterns (LBP) for feature extraction. The extensive experiment are conducted on largest publicly available benchmark dataset such as PH2, Kaggel and HAM 10000. To validate the performance of proposed technique when compared with traditional segmentation and classification techniques. The proposed system archive 97% of accuracy, 98% sensitivity and 96% of specificity for PH2 dataset. The planned scheme stands out among the few modern literary sources presented in the context of the analysis of DS images in terms of productivity and accepted methodologies, which proves the reliability of the novel study.