K-nearest Neighbor Clustering Research Articles

Investigations on Brain Tumor Classification Using Hybrid Machine Learning Algorithms.

The imaging modalities are used to view other organs and analyze different tissues in the body. In such imaging modalities, a new and developing imaging technique is hyperspectral imaging. This multicolour representation of tissues helps us to better understand the issues compared to the previous image models. This research aims to analyze the tumor localization in the brain by performing different operations on hyperspectral images. The tumor is located using the combination of k-based clustering processes like k-nearest neighbour and k-means clustering. The value of k in both methods is determined using the optimization process called the firefly algorithm. The optimization processes reduce the manual calculation for finding K's optimal value to segment the brain regions. The labelling of the areas of the brain is done using the multilayer feedforward neural network. The proposed technique produced better results than the existing methods like hybrid k-means clustering and parallel k-means clustering by having a higher peak signal-to-noise ratio and a lesser mean absolute error value. The proposed model achieved 96.47% accuracy, 96.32% sensitivity, and 98.24% specificity, which are improved compared to other techniques.

SRIQ clustering: A fusion of Random Forest, QT clustering, and KNN concepts

Gene expression profiling together with unsupervised analysis methods, typically clustering methods, has been used extensively in cancer research to unravel, e.g., new molecular subtypes that hold promise of disease refinement that may ultimately benefit patients. However, many of the commonly used methods require a prespecified number of clusters to extract and frequently require some type of feature pre-selection, e.g. variance filtering. This introduces subjectivity to the process of cluster discovery and the definition of putative novel tumor subtypes. Here, we introduce SRIQ, a novel unsupervised clustering method that could circumvent some of the issues in commonly used unsupervised analysis methods. SRIQ incorporates concepts from random forest machine learning as well as quality threshold- and k-nearest neighbor clustering. It is implemented as a Java and Python pipeline including data pre-processing, differential expression analysis, and pathway analysis. Using 434 lung adenocarcinomas profiled by RNA sequencing, we demonstrate the technical reproducibility of SRIQ and benchmark its performance compared to the commonly used consensus clustering method. Based on differential gene expression analysis and auxiliary molecular data we show that SRIQ can define new tumor subsets that appear biologically relevant and consistent compared and that these new subgroups seem to refine existing transcriptional subtypes that were defined using consensus clustering. Together, this provides support that SRIQ may be a useful new tool for unsupervised analysis of gene expression data from human malignancies.

Pear Defect Detection Method Based on ResNet and DCGAN

To address the current situation, in which pear defect detection is still based on a workforce with low efficiency, we propose the use of the CNN model to detect pear defects. Since it is challenging to obtain defect images in the implementation process, a deep convolutional adversarial generation network was used to augment the defect images. As the experimental results indicated, the detection accuracy of the proposed method on the 3000 validation set was as high as 97.35%. Variant mainstream CNNs were compared to evaluate the model’s performance thoroughly, and the top performer was selected to conduct further comparative experiments with traditional machine learning methods, such as support vector machine algorithm, random forest algorithm, and k-nearest neighbor clustering algorithm. Moreover, the other two varieties of pears that have not been trained were chosen to validate the robustness and generalization capability of the model. The validation results illustrated that the proposed method is more accurate than the commonly used algorithms for pear defect detection. It is robust enough to be generalized well to other datasets. In order to allow the method proposed in this paper to be applied in agriculture, an intelligent pear defect detection system was built based on an iOS device.

An Optimized Nature-Inspired Metaheuristic Algorithm for Application Mapping in 2D-NoC.

Mapping application task graphs on intellectual property (IP) cores into network-on-chip (NoC) is a non-deterministic polynomial-time hard problem. The evolution of network performance mainly depends on an effective and efficient mapping technique and the optimization of performance and cost metrics. These metrics mainly include power, reliability, area, thermal distribution and delay. A state-of-the-art mapping technique for NoC is introduced with the name of sailfish optimization algorithm (SFOA). The proposed algorithm minimizes the power dissipation of NoC via an empirical base applying a shared k-nearest neighbor clustering approach, and it gives quicker mapping over six considered standard benchmarks. The experimental results indicate that the proposed techniques outperform other existing nature-inspired metaheuristic approaches, especially in large application task graphs.

Different spreading dynamics throughout Germany during the second wave of the COVID-19 pandemic: a time series study based on national surveillance data.

BackgroundThe second wave of the COVID-19 pandemic led to substantial differences in incidence rates across Germany.MethodsAssumption-free k-nearest neighbour clustering from the principal component analysis of weekly incidence rates of German counties groups similar spreading behaviour. Different spreading dynamics was analysed by the derivative plots of the temporal evolution of tuples [x(t),x’(t)] of weekly incidence rates and their derivatives. The effectiveness of the different shutdown measures in Germany during the second wave is assessed by the difference of weekly incidences before and after the respective time periods.FindingsThe implementation of non-pharmaceutical interventions of different extents resulted in four distinct time periods of complex, spatially diverse, and age-related spreading patterns during the second wave of the COVID-19 pandemic in Germany. Clustering gave three regions of coincident spreading characteristics. October 2020 showed a nationwide exponential growth of weekly incidence rates with a doubling time of 10 days. A partial shutdown during November 2020 decreased the overall infection rates by 20–40% with a plateau-like behaviour in northern and southwestern Germany. The eastern parts exhibited a further near-linear growth by 30–80%. Allover the incidence rates among people above 60 years still increased by 15–35% during partial shutdown measures. Only an extended shutdown led to a substantial decrease in incidence rates. These measures decreased the numbers among all age groups and in all regions by 15–45%. This decline until January 2021 was about -1•25 times the October 2020 growth rates with a strong correlation of -0•96.InterpretationThree regional groups with different dynamics and different degrees of effectiveness of the applied measures were identified. The partial shutdown was moderately effective and at most stopped the exponential growth, but the spread remained partly plateau-like and regionally continued to grow in a nearly linear fashion. Only the extended shutdown reversed the linear growth.FundingInstitutional support and physical resources were provided by the University Witten/ Herdecke and Kliniken der Stadt Köln, German ministry of education and research ‘Netzwerk Universitätsmedizin’ (NUM), egePan Unimed (01KX2021).

Different Spreading Dynamics Throughout Germany During the Second Wave of the COVID-19 Pandemic: Link to Public Health Interventions

Background: The second wave of the COVID-19 pandemic led to substantial differences in incidence rates across Germany. Methods: Assumption-free k-nearest neighbour clustering from the principal component analysis of weekly incidence rates of German counties groups similar spreading behaviour. Different spreading dynamics was analysed by the derivative plots of the temporal evolution of tuples [x(t),x’(t)] of weekly incidence rates and their derivatives. The effectiveness of the different shutdown measures in Germany during the second wave is assessed by the difference of weekly incidences before and after the respective time periods. Findings: The implementation of non-pharmaceutical interventions of different extents resulted in four distinct time periods of complex, spatially diverse, and age-related spreading patterns during the second wave of the COVID-19 pandemic in Germany. Clustering gave three regions of coincident spreading characteristics. October 2020 showed a nationwide exponential growth of weekly incidence rates with a doubling time of 10 days. A partial shutdown during November 2020 decreased the overall infection rates by 20–40% with a plateau-like behaviour in northern and southwestern Germany. The eastern parts exhibited a further near-linear growth by 30–80%. Allover the incidence rates among people above 60 years still increased by 15–35% during partial shutdown measures. Only an extended shutdown led to a substantial decrease in incidence rates. These measures decreased the numbers among all age groups and in all regions by 15–45%. This decline until January 2021 was about -1∙25 times the October 2020 growth rates with a strong correlation of -0∙96. Interpretation: Three regional groups with different dynamics and different degrees of effectiveness of the applied measures were identified. The partial shutdown was moderately effective and at most stopped the exponential growth, but the spread remained partly plateau-like and regionally continued to grow in a nearly linear fashion. Only the extended shutdown reversed the linear growth.Funding Statement: Institutional support and physical resources were provided by the University Witten/ Herdecke and Kliniken der Stadt Köln, German ministry of education and research ‘Netzwerk Universitätsmedizin’ (NUM), egePan Unimed (01KX2021).Declaration of Interests: Dr. Karagiannidis reports personal fees from Maquet, personal fees from Xenios, personal fees from Bayer, non-financial support from Speaker of the German register of ICUs, grants from German Ministry of Research and Education, during the conduct of the study. Dr. Schuppert reports grants from Bayer AG, outside the submitted work. Dr. Jens Karschau has nothing to disclose. Dr. Polotzek has nothing to disclose. Dr. Schmitt reports personal fees from Sanofi, Lilly, ALK, Novartis, grants from Sanofi, Pfizer, ALK, Novartis, outside the submitted work. Dr. Busse reports grants from Berlin University Alliance, non-financial support from German Federal Ministry of Health, during the conduct of the study.

Online Methodology for Separating the Power Consumption of Lighting Sockets and Air-Conditioning in Public Buildings Based on an Outdoor Temperature Partition Model and Historical Energy Consumption Data

Among sub-items of energy consumption in public buildings, lighting sockets play an important role in energy-saving analysis. So, the energy consumption data quality of lighting sockets is important. However, limited by the initial cost of energy monitoring platform, it is difficult to install electricity meters covering all branches and to retrofit the incompact classification electricity branches, which results in a mixture of the lighting socket energy consumption and other components. In this study, a separation methodology is proposed. First, the abnormal data in the energy monitoring platform are cleaned and screened using a clustering algorithm. Second, the average outdoor air temperature partitioning model (OATPM) method and the k-nearest neighbor (KNN) clustering algorithm method are proposed for identifying and separating the abnormal data. These two methods have complementary advantages in the best applicable scenarios, including calculation accuracy and other aspects. The verification results for three buildings show that the relative error of this separation methodology is less than 15%. Finally, this paper presents the optimization parameters of the KNN method. Through this methodology, building managers need only historical data in an energy monitoring platform to separate the combined power consumption of the lighting sockets and air-conditioning online, independent of detailed information statistics.

Diagnostic of pathology on the vertebral column machine learning - Cluster K-nearest Neighbor (CKNN) part (I)

In this investigation, we have developed a graphical user interface application to perform the diagnostic of pathology on the column vertebral based on the Cluster K-Nearest Neighbor (CKNN) classifier. The system is implemented and simulated in Anaconda, and its performance is tested on real dataset that contains 6 features and two (02) classes. Each class, abnormal and normal class consists of 210 instances, and 100 instances, respectively. A comparison of the performance of the test measurement under various test sizes (10%~50%) is carried out to predict the class label when the nearest neighbor k changes from 1 to 19. The results show that the accuracy depends on both independent parameters, the test size and k-neighbors, which gives better training accuracy than the test accuracy, in the range of [82.5% ~ 100%] and [70%~84%], respectively. When k varies from 1 to 4, a higher training accuracy, larger than 90% is observed. While the test set shows a low accuracy in the range of [74% ~ 82.5%]. Increasing the test size or/and k, does not affect significantly the accuracy. When k is larger 1, the training accuracy is approximately equal to 0.925±0.05, the test accuracy (except for k=6 and 17) is about 0.79±0.05. The prediction of the class status maybe optimized by combining the dataset set size with the k-neighbors parameters. The GUI can be useful to help the medical doctors to diagnostic the patient effectively to take a rapid decision and predict results in a reduced time lapse.

Diagnostic of pathology on the vertebral column machine learning - Cluster K-nearest Neighbor (CKNN) part (I)

Diagnostic of pathology on the vertebral column machine learning - Cluster K-nearest Neighbor (CKNN) part (I)

An adaptive new state recognition method based on density peak clustering and voting probabilistic neural network

The traditional fault diagnosis method based on classifiers relies heavily on complete prior knowledge of the existing faults and often requires complex denoising calculations. However, in practical applications, it is difficult to obtain the complete fault state data, especially degradation state data, in advance. This reduces the classification accuracy of unknown new states and severely limits fault diagnosis. To overcome these difficulties, a novel analog circuit fault diagnosis and unknown state recognition method based on density peak clustering and voting probabilistic neural network (VPNN) is presented. In this method, the novel VPNN model is constructed based on prior knowledge of faults, in which a majority voting algorithm is applied instead of the complex process of denoising, to restrain the environmental noise. Further, a newly designed discriminant layer is added after the summation layer to identify the unknown state data, pursuant to which a derivative-based method is proposed to eliminate the transition data. Moreover, to update the preliminary VPNN, the K-nearest neighbor and density peak clustering procedures are applied to automatically determine the number of new pattern neuron classes, and a data reduction algorithm based on the Gaussian mixture model is proposed to determine the pattern neuron samples. Accordingly, numerous redundant samples are reduced. Thus, new pattern neurons can be straightforwardly added to the preliminary VPNN. Our experimental results clearly demonstrate the robustness of the proposed method, which can reduce false alarms, identify unknown states, and determine the newly added pattern neuron classes and pattern neuron samples to update the VPNN automatically.

Predictive Model for Risk of 30-Day Rehospitalization Using a Natural Language Processing/Machine Learning Approach Among Medicare Patients with Heart Failure

IntroductionNearly 80% of all patients with heart failure (HF) are older adults (≥65 years of age). Prior studies have built predictive models that relied on structured data from electronic health records (EHRs) to predict the risk of 30-day rehospitalization for patients with HF. Structured data mostly included simple vocabularies such as age, and ethnicity. Rarely do prior studies include clinical narrative data in a free-text format (i.e., unstructured data). No previous study has focused on using clinical narrative notes specifically for Medicare patients with HF in the acute-care setting.AimTo identify clinical notes for building a predictive model for risk of 30-day rehospitalization among Medicate patients with HF.MethodsThis study first used free-text discharge summary notes and nursing care plans collected from June 1, 2015 to December 31, 2019, for a randomly selected 500 Medicare patients with HF. Natural Language Processing (NLP): we iterated over standard text pre-processing steps, exploring the impact of n-gram length, term document-frequency, word stemming, and the added value of parts-of-speech. We chose two models: 1) the classification model called Bag-of Words (BOW), where each document is represented by a vector based on the pre-processed text, and 2) Document Embedding, where document terms are mapped to a dimension-reducing layer (length equals 300). The latter runs exceptionally fast (on the order of tens-of-seconds for 2,000 documents). Machine Learning (ML): the output of the NLP BOW and Document Embedding models were fed to six different conventional machine learning systems (logistic regression, support vector machine, random forest, k-nearest neighbor clustering, a three-layer neural network, and Naïve Bayes).ResultsThe mean age was 77±7.9, and the average of length of hospital stay was 4.9 days ± 4.8. The best BOW model we found using discharge summaries (n=387) produced an Area Under the Receiver Operating Characteristics Curve (AUC) of 0.71 and F1 score of 0.65. The best Document Embedding model yielded an AUC of 0.65 and an F1 score of 0.61. Using nursing care notes as the unit of analysis (n = 2,046), the NLM/ML performed far better. The best BOW model on care plans found an AUC of 0.85 and F1 score of 0.77. The best Document Embedding produced an AUC of 0.83 and an F1 score of 0.75. In all cases we held out 33% of the data set for validation, repeating a random draw 10 times and averaging the performance results.ConclusionsWe conclude that nursing care plans are a better predictor of 30-day rehospitalization risk than discharge summaries. Because nursing care plans are shorter than discharge summaries, they have the added advantage of faster processing. Since the faster Document Embedding model's performance is comparable to that of BOW, we suggest its use in future work in the area of 30-day rehospitalization risk in Medicare patients with HF.

Data Mining Approach to Effort Modeling On Agile Software Projects

Software production is a complex process. Accurate estimation of the effort required to build the product, regardless of its type and applied methodology, is one of the key problems in the field of software engineering. This study presents the approach to effort estimation on agile software project using local data and data mining techniques, in particular k-nearest neighbor clustering algorithm. The applied process is iterative, meaning that in order to build predictive models, sets of data from previously executed project cycles are used. These models are then utilized to generate estimate for the next development cycle. Used data enrichment process, proved to be useful as results of effort prediction indicate decrease in estimation error compared to the estimates produced solely by the estimators. The proposed approach suggests that similar models can be built by other organizations as well, using the local data at hand and this way optimizing the management of the software product development.

Green Computing Process and its Optimization Using Machine Learning Algorithm in Healthcare Sector

Handling the information is crucial task in healthcare sector; the data mining techniques will be right choice to address the complex problems. The hybridized optimization techniques in big data analytics consider the important part of the healthcare network communication issues in decision making approach of patient information. This article focused on heart disease data mining and relevant issues since the heart diseases are considered as a reason for causing deaths just as for males and females all over the world. So, people need to be conscious of possible aspects of heart disease. Even though genetics has a part, some of the standards of living practiced are the fundamental reasons for the heart disease. The heart diseases are classified by classical techniques with 13 risk factors and helpful variables. The introduced approach delivers a new computing hybrid modeling scheme for detect the heart diseases. This study represents, various existing methods making decisions for cardio vascular risks depends on the artificial neural networks (ANN). This ANN based methods generally anticipated that Heart Failure attributes having same risk involvement to the heart failure diagnosis. In this article the strategy of an effective recognition method is analyzed for analyzing the failure related to heart diseases using a hybridized approach of K-Nearest Neighbor clustering and Spiral optimization in the classification of the cardio vascular risks. The hybridized KNN technique is matched with some data mining techniques like Support vector Machine (SVM), Convolutional Neural Networks (CNN), and Artificial Neural Networks (ANN). The experimental results of this work achieved optimized improved results significantly than other machine learning techniques. The illustrative results exposed that the hybrid scheme stated effectually classify heart disease in the way of computing optimized prediction of heart diseases. Overall the proposed algorithm evidence 5% of enhancement in prediction of heart diseases with comparison of other existing machine learning techniques.

Cumulative belief peaks evidential K-nearest neighbor clustering

This paper introduces a new evidential clustering algorithm based on finding the “cumulative belief peaks” and evidential K-nearest neighbor rule. The basic assumption of this algorithm is that a cluster center has the highest cumulative possibility of becoming a cluster center among its neighborhood and size of its neighborhood is relatively large. To measure such cumulative possibility, a new notion of cumulative belief is proposed in the framework of belief functions. By maximizing an objective function, an appropriate size of the relatively large neighborhood is determined. Then, the objects with highest cumulative belief among their own neighborhood of this size are automatically detected as cluster centers. Finally, a credal partition is derived by evidential K-nearest neighbor rule with the fixed cluster center. Experimental results show that the proposed evidential clustering algorithm can automatically detect cluster centers and well reveal the data structure in form of a credal partition in tolerable time, when tackling datasets with small number of data objects and dimensions. As the sizes of datasets increase, running time of such new clustering algorithm increases sharply and this reduces the practicability of it. Simulations on synthetic and real-world datasets validate our conclusions.

An Intelligent Big Data Analytics System using Enhanced Map Reduce Techniques

An Intelligent Big Data Analytics System using Enhanced Map Reduce Techniques include a set of Methods, applications and strategy which helps the organization and industry to bring together the data and information from outside sources and internal systems, as well as it is used to collect , classify, analysis and run the queries against the data and prepare the report for effective decision making. The Enhanced Map Reduced Techniques based on K-Nearest Neighbor (KNN) clustering Strategy works efficient as well as in an effective manner. We found that the existing MR – mafia sub space clustering Strategy have not performed effectively .Many clustering techniques are adopted in real world data analysis for example customer behavior analysis, medical data analysis, digital forensics, etc. The existing MR- mafia sub space clustering Strategy is inefficient because of continuously increase in the data size, and overlaying of the data blocks .The proposed KNN clustering Strategy mainly focused on the enhanced the Map Reduce techniques, and then to avoid the unnecessary input and output data, optimize the data storage in order to achieve the best out sourcing of data privacy. The proposed KNN clustering Strategy works effectively and that can be outsourced to cloud server.

Hierarchical Clustering Algorithm Based on Density Peaks using Kernel Function for Thalassemia Classification

Thalassemia is an inherited blood disorder and one of the most common genetic diseases in the world, with its classification due to the processes involved in patients’ treatment. Therefore, it is essential to obtain a precise diagnosis due to an appropriate treatment receives a higher lifetime value. A new method based on the kernel, modified from hierarchical clustering based on density peaks (HCDP) was proposed in this research. Furthermore, the concept of k-nearest neighbor and hierarchical clustering, consists of three steps were utilized. These steps include local density calculation, hierarchy representation, and optimal cluster extraction. The modification of this technique is therefore based on the polynomial kernel function, which was expected to be more accurate in separating data that cannot be detached linearly. Data utilized was obtained from Harapan Kita Hospital, West Jakarta, Indonesia, and it consists of 82 thalassemia and 68 non-thalassemia samples. Using the proposed method, the performance of HCDP with/without kernel function in this paper were examined using 10-fold cross-validation and compared using the confusion matrix by calculating its F1-Score. The results concluded that hierarchical clustering based on density peaks gives approximately 67.77 percent F1-Score, while 70.06 percent is obtained when the method is combined with the kernel function.

Detection and Classification of Breast Cancer in Mammography Images Using Pattern Recognition Methods

Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features. Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other normal parts of the breast image. In this study, 19 final different features of each image were extracted to generate the feature vector for classifier input. The proposed method not only determined the boundary of masses but also classified the type of masses such as benign and malignant ones. The neural network classification methods such as the radial basis function (RBF), probabilistic neural network (PNN), and multi-layer perceptron (MLP) as well as the Takagi-Sugeno-Kang (TSK) fuzzy classification, the binary statistic classifier, and the k-nearest neighbors (KNN) clustering algorithm were used for the final decision of mass class. Results: The best results of the proposed method for accuracy, sensitivity, and specificity metrics were obtained 97%±4.36, 100%±0 and 96%±5.81, respectively for support vector machine (SVM) classifier. Conclusions: By comparing the results of the proposed method with the results of the other previous methods, the efficiency of the proposed algorithm was reported.

Multi-Objective Optimization of Time-of-Use Price for Tertiary Industry Based on Generalized Seasonal Multi- Model Structure

In the smart grid, the demand-side management of power load plays an essential role to improve power load curve where time-of-use (TOU) price incentive is widely used to mobilize the multi-time-scale response ability of demand-side. Considering the seasonal and daily characteristics of user power load, in this paper, the dynamic multi-objective optimization of TOU price under multi-model structure is mainly studied. First, the preprocessing of raw power load data is systematically discussed, including abnormal data elimination and missing data reconstruction. Then, combining adaptive affinity propagation (adAP) clustering and fuzzy k-nearest neighbor (FKNN) clustering, generalized seasons are divided while peak-flat-valley time periods are redivided for each season. Subsequently, a frequency-separated demand response model structure involving steady-state and uncertainty terms is built to approach actual user load response with balanced model complexity and accuracy. For each seasonal model using this structure, the multi-objective optimization problem of TOU price is formed where non-dominated sorting genetic algorithm-II (NSGA-II) with probabilistic deviation sorting order strategy is proposed to select the balance optimal solutions. Finally, the effectiveness of the above methods is validated. The simulation results show that if the optimized seasonal TOU prices are adopted, the power load curve can be obviously improved increasing load rate, reducing electricity price expenditure, and ensuring user satisfaction degree.

A Self-Adaptive Mapping Approach for Network on Chip With Low Power Consumption

Application mapping of disseminated intellectual property into Network on Chip (NoC) is a well-defined NP-Hard problem. Improvement of network performance in NoC is purely based on an effective mapping approach with cost and performance metrics optimization which includes area, power, delay, reliability, and thermal distribution. A self-adaptive mapping approach for NoC is proposed in this paper. In this method, the self-adaptive chicken swarm optimization algorithm (SCSO) is used for an effective mapping, which has never been applied with NoC. The proposed method reduces the power consumption of NoC through a cognitive base using shared K-nearest neighbor clustering method and it offers faster mapping over standard and randomly generated benchmarks. The experimental results indicate that the proposed method outperforms existing bio-inspired metaheuristic algorithms, especially for large application graph.

Performance study of K-nearest neighbor classifier and K-means clustering for predicting the diagnostic accuracy

The major challenge related to data management lies in healthcare sector due to increase in patients proportional to the population growth and change in lifestyle. The data analytics and big data are becoming trends to provide solution to all analytical problems that can be obtained by using machine learning techniques. Today, cancer is evolving as one of the major attention seeking phenomenon in developed as well as in developing countries that may lead to death if not diagnosed at the early stage. The late diagnosis, and hence delayed treatment increase the risk for the survival. Thus, early detection to improve the cancer outcome is very critical. This study is intended towards early diagnosis of cancer using more efficient analytical techniques. Moreover, accuracy plays an important role in prediction to improve the quality of care, thereby increasing the survival rate. For this study, the datasets are extracted from UCI Machine Learning Repository prepared by University of Wisconsin Hospitals. For the diagnosis and classification process, K Nearest Neighbor (KNN) classifier is applied with different values of K variable, introducing the process called KNN Clustering. Later the performance of KNN is compared with K-Means clustering on the same datasets.