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A dynamic model using k-nn algorithm for predicting diabetes and breast cancer

Computers in Biology and Medicine 2025年第PT B期192卷 110276-110276页

作者： Hussein A.A. Al-khamees Nor Samsiah Sani Ahmed Sileh Gifal Luan Xiang Wei Liu Mohd Isrul Esa Computer Techniques Engineering Department College of Engineering and Technology Al-Mustaqbal University 51001 Babil Iraq Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Selangor Malaysia Department of Information Technology College of Computer Science and Information Technology University of Kerbala 56001 Karbala Iraq

Healthcare remains a critical focus due to its direct impact on human well-being. Diabetes, currently the fastest-growing chronic disease globally, poses severe health risks, including cardiovascular complications and kidney failure. Simultaneously, breast cancer has become the most prevalent cancer among women, particularly those in their 40s, surpassing other types. Early detection and diagnosis of these two diseases remain a substantial challenge, yet they are crucial for reducing mortality rates. Machine learning algorithms emerged as powerful tools in healthcare for disease classification and prediction, with the k-nearest neighbors (k-nn) being one of the most widely used supervised learning algorithm. Different traditional machine learning methods have been proposed, which are heavily specialized for specific datasets. More deeply, traditional k-nn relies on a static k-value, which may not provide optimal results across diverse datasets. This paper proposes a dynamic k-nn model that adjusts ‘k’ value based on local data characteristics, enhancing prediction accuracy. The proposed model is testing on two publicly available datasets; PIMA Diabetes and Breast Cancer Wisconsin (BCW) datasets. Our results are evaluated using different metrics that are; accuracy, precision, recall, F1_score, and execution time. The results of these metrics are as follows; (81.17%, 97.37%), (83.33% 100%), (54.55%, 86.05%), and (65.93%, 92.5%) for PIMA and BCW datasets respectively. These results demonstrate that the proposed model outperformed several state-of-the-art models. Thus, further highlighting its effectiveness and efficiency in medical data classification.

关键词： Machine learning Diabetes Breast cancer k-nn algorithm PIMA dataset Breast Cancer Wisconsin (BCW) dataset

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Preliminary Analysis of RFID Localization System for Moving Precast Concrete Units using Multiple-Tags and Weighted Euclid Distance k-nn algorithm 15

Preliminary Analysis of RFID Localization System for Moving ...

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IEEE International Conference on RFID (RFID)

作者： Durtschi, Barrett Mahat, Mahesh Mashal, Mustafa Chrysler, Andrew Idaho State Univ Elect & Comp Engn Pocatello ID 83209 USA Idaho State Univ Civil & Environm Engn Pocatello ID 83209 USA

ISBN: (纸本)9781665440905

This paper presents two RFID localization methods based on a k-nn algorithm for multiple moving tracking tags attached to a concrete masonry unit (cinder block). This work uses passive RFID tags for localization and seeks to provide rapid wireless analysis for future smart infrastructure projects where precast concrete modular structures are moved during transport and assembly. The RFID localization system uses four reader antennas, four tracking tags, and 28 reference tags in a realistic indoor assembly environment. Results show average error in the direction of movement as low as 10.5 cm. Increasing the number of nearest neighbors in the k-nn algorithm is shown to reduce error in all coordinate directions. Increasing k from 4 to 6 is shown to reduce error by 4 cm or 10%. The localization environment is analyzed, and reference tags 22, 9, 5, and 8 around the moving cinder block are seen most commonly as nearest neighbors. A modified k-nn algorithm, described here as a weighted Euclidian distance k-nn algorithm is presented that reduces total error from 41.1 cm to 32.5 cm.

关键词： RFID Localization k-nn algorithm

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Revisiting the k-nn algorithm for obstetric image segmentation

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International Journal of Hybrid Intelligent Systems 2021年第1-2期17卷 1-13页

作者： Salgado, P. Azevedo Perdicoúlis, T.-P. CITAB-Department de Engenharias Ect Utad Quinta de Prados Vila Real Portugal Department of Electrical and Computer Engineering Ect Utad of Institute of Systems and Robotics University of Coimbra Coimbra Portugal

Medical image techniques are used to examine and determine the well-being of the foetus during pregnancy. Digital image processing (DIP) is essential to extract valuable information embedded in most biomedical signals. Afterwords, intelligent segmentation methods, based on classifier algorithms, must be applied to identify structures and relevant features from previous data. The success of both is essential for helping doctors to identify adverse health conditions from the medical images. To obtain easy and reliable DIP methods for foetus images in real-time, at different gestational ages, aware pre-processing needs to be applied to the images. Thence, some data features are extracted that are meant to be used as input to the segmentation algorithms presented in this work. Due to the high dimension of the problems in question, assemblage of the data is also desired. The segmentation of the images is done by revisiting the k-nn algorithm that is a conventional nonparametric classifier. Besides its simplicity, its power to accomplish high classification results in medical applications has been demonstrated. In this work two versions of this algorithm are presented (i) an enhancement of the standard version by aggregating the data apriori and (ii) an iterative version of the same method where the training set (TS) is not static. The procedure is demonstrated in two experiments, where two images of different technologies were selected: a magnetic resonance image and an ultrasound image, respectively. The results were assessed by comparison with the k-means clustering algorithm, a well-known and robust method for this type of task. Both described versions showed results close to 100% matching with the ones obtained by the validation method, although the iterative version displays much higher reliability in the classification. © 2021 - IOS Press. All rights reserved.

关键词： Feature extraction image segmentation k-nn algorithm magnetic resonance image ultrasound image

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Fermentation Level Classification of Cross Cut Cacao Beans Using k-nn algorithm 18

Fermentation Level Classification of Cross Cut Cacao Beans U...

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5th International Conference on Bioinformatics Research and Applications (ICBRA)

作者： Angelia, Randy E. Linsangan, Noel B. Univ Mindanao Comp Engn Program Coll Engn Educ Davao Philippines Mapua Univ Sch Elect Elect & Comp Engn Manila Philippines

ISBN: (纸本)9781450366113

In chocolate production, post-harvest procedure is one of the most critical factors. Fermentation is a vital procedure to consider since exact generation of acid contemplate to aroma and quality of the final product. This innovative study aims to classify the quality of the cacao beans after the post-harvest procedures. Classified sample beans from partner cacao trader were analyzed and became data sets of the device. Photographs are taken to the subjects and undergo image processing procedure then through k-Nearest Neighbors algorithm (k-nn). Beans are classified to be well-fermented under fermentation and over-fermentation process. Function test and statistical analysis using confusion matrix revealed 97.22 percent accuracy in analyzing well-fermented beans, 92.59 percent accuracy in under fermented, 75 percent in over-fermented and 80 percent in analyzing unknowns. These results generated 92.50 percent overall accuracy of the device.

关键词： Cacao quality k-nn algorithm Cacao Bean Agricultural Technology Image Classification

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Artificial intelligent matching for scratches of semiconductor wafers based on a k-nn algorithm

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SURFACE TOPOGRAPHY-METROLOGY AND PROPERTIES 2019年第2期7卷 025012-025012页

作者： Pan, Tingting Yang, Jie Wu, Wei Dung, Zhigang Ayinde, Babajide O. Dalian Univ Technol Sch Math Sci Dalian 116024 Peoples R China Dalian Univ Technol Minist Educ Key Lab Precis & Nontradit Machining Dalian 116024 Peoples R China Univ Louisville Elect & Comp Engn Louisville KY 40292 USA

Scratches, those usually generated during polishing the silicon wafer surface, are one of the major yield loss factors in semiconductor manufacturing industry. In order to determine the source of the scratches in real time and reduce the yield loss, it is critical for manufacturers to match and identify the same type of scratches automatically. In this paper, an improved k nearest neighbors (knn) algorithm to address this issue is presented. Firstly, a skeleton extraction method is used to depict the main lines of scratches. Then the clustering protocol is applied as a preliminary step to group these main lines so that some essential endpoints features of main lines, such as distance, slope and curvature, can be extracted. During feature extraction, a dynamic coordinate system is introduced and this greatly reduces the distortions arise due to the magnitude of tangent difference. An intelligent matching of similar scratches MSML-knn algorithm is formulated. The experimental results show that the proposed matching method for wafer scratches has a good adaptability and robustness.

关键词： semiconductor wafer k-nn algorithm scratches matching

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The k-nn algorithm for Compositional Data: A Revised Approach with and Without Zero Values Present

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Journal of Data Science 2014年第3期12卷 519-534页

作者： Michail Tsagris School of Mathematical Sciences University of Nottingham

In compositional data, an observation is a vector with non-negative components which sum to a constant, typically 1. Data of this type arise in many areas, such as geology, archaeology, biology, economics and political science among others. The goal of this paper is to extend the taxicab metric and a newly suggested metric for com-positional data by employing a power transformation. Both metrics are to be used in the k-nearest neighbours algorithm regardless of the presence of zeros. Examples with real data are exhibited.

关键词： compositional data entropy k-nn algorithm metric supervised classification

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Fraud Detection in Shipping Industry using k-nn algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2021年第4期12卷 466-475页

作者： Subramaniam, Ganesan Mahmoud, Moamin A. Univ Tenaga Nas Coll Comp & Informat Kajang Malaysia Univ Tenaga Nas Inst Informat & Comp Energy IICE Kajang Malaysia

The shipment industry is going through tremendous growth in volume thanks to technological innovation in e-commerce and global trade liberalization. Volume growth also means a rise in fraud cases involving smuggling and false declaration of shipments. Shipping companies and customs are mostly relying on routine random inspection thus finding fraud is often by chance. As the volume increases dramatically it would no longer be sustainable and effective for both shipment companies and customs to pursue traditional fraud detection strategies. Other related papers on this area have proven that intelligent data-driven fraud detection is proven to be far more effective than routine inspections. However, the challenge in data-driven detection is its effectiveness are often reliant on the availability of data and the various fraud mechanism used by fraudsters to commit shipment related fraud. As such in this paper, we review and subsequently identify the most optimized approaches and algorithms to detect fraud effectively within the shipping industry. We also identify factors that influence fraud activity, review existing fraud detection models, develop the detection framework and implement the framework using the Rapidminer tool.

关键词： Fraud detection shipping industry k-nn algorithm

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AN OPTIMIZATION algorithm OF k-nn CLASSIFICATION

<bold>AN OPTIMIZATION ALGORITHM OF K-NN CLASSIFICATION</bold...

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5th International Conference on Machine Learning and Cybernetics

作者： Zhan, Yan Chen, Hao Zhang, Guo-Chun Hebei Univ Fac Math & Comp Sci Machine Learning Ctr Baoding 071002 Hebei Peoples R China

ISBN: (纸本)1424400600

k-nearest neighbor (k-nn) algorithm is a classification method based on statistical theory. In this algorithm the Euclidean distance is usually chosen as the similarity measure, which usually relates to all attributes. Accordingly one practical issue in applying k-nn algorithm is that the distance between instances is calculated based on all attributes of the instance. One interesting approach to overcoming this problem is to weight each attribute differently when calculating the distance between two instances. So we can decide different functions of each feature by using feature weight learning. Another issue is that we still need evaluate k value by testing different values. In order to avoid searching for k value in nearest neighbor experiment and make the accuracy and efficiency more perfect, we bring forward one validity function in this paper for judging clustering when the classification of data set is clear. We apply it into classification problem such as k-nn combining with supervised classification. Thus we can only select the nearest neighbor (1-nn) not only to achieve more precise classification but also to avoid the trouble of looking for k, which will reduce the query complexity greatly and improve the efficiency of nearest neighbor algorithm. Simultaneously, the Nearest Neighbor algorithm is one of the most basic Case-Base Reasoning (CBR) problems and Case-Base Maintenance (CBM) is an important issue in CBR System to obtain the efficient case bases. This paper proposes a new approach to select representative cases based on Generalization Capability of cases. Using this method, most redundant cases, which affect the solution accuracy, can be deleted. It will improve indexing efficiency in searching near neighbors.

关键词： k-nn algorithm similarity metrics feature weight clustering validity case-base maintenance generalization capability

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Identification of Diabetes Level Using AI/ML Model

Identification of Diabetes Level Using AI/ML Model

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2025 IEEE International Conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Purkar, Pratiksha Arun Londhe, Pranav Vitthal Pathave, kushal Bhoraji Bhimanpallewar, Ratnmala N. Mirajkar, Riddhi Vishwakarma Institute of Information Technology Department of Information Technology Pune411046 India

ISBN: (纸本)9798331512965

Diabetes has become a critical public health challenge due to its alarming prevalence and severe complications. Predictive models are required for early diabetes detection to mitigate the impacts. The present study used k-nearest neighbor algorithm assessment regarding how age, body mass index, blood pressure, and physical activity affect diabetogenic risk. The different health data analyzed prove the effectiveness of k-nn for diagnosing those who are prone to diabetes. The model tries to classify people based on the above-named critical health indicators. Age and body mass index have an above average influence on blood pressure and amount of physical activity according to the k-nn model, thus this model could be used for effective stratification of risk related to diabetes as it scored 0.96. This confirms the utility of k-nn in providing very insightful information for assessing diabetes risk. © 2025 IEEE.

关键词： Diabetes prediction health care k-nn algorithm Machine learning

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A Low-Carbon Decision-Making algorithm for Water-Spot Tourists, Based on the k-nn Spatial-Accessibility Optimization Model

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WATER 2022年第18期14卷 2920页

作者： Zhou, Xiao Wen, Bowei Su, Mingzhan Tian, Jiangpeng Leshan Vocat & Tech Coll Postdoctoral Innovat Practice Base Sichuan Prov Leshan 614000 Peoples R China PLA Strateg Support Force Informat Engn Univ Inst Geospatial Informat Zhengzhou 450001 Peoples R China

This study presents a low-carbon decision-making algorithm for water-spot tourists, based on the k-nn spatial-accessibility optimization model, to address the problems of water-spot tourism spatial decision-making. The attributes of scenic water spots previously visited by the tourists were knowledge-mined, to ascertain the tourists' interest-tendencies. A scenic water-spot classification model was constructed, to classify scenic water spots in tourist cities. Then, a scenic water spot spatial-accessibility optimization model was set up, to sequence the scenic spots. Based on the tourists' interest-tendencies, and the spatial accessibility of the scenic water spots, a spatial-decision algorithm was constructed for water-spot tourists, to make decisions for the tourists, in regard to the tour routes with optimal accessibility and lowest cost. An experiment was performed, in which the tourist city of Leshan was chosen as the research object. The scenic water spots were classified, and the spatial accessibility for each scenic spot was calculated;then, the optimal tour routes with optimal spatial accessibility and the lowest cost were output. The experiment verified that the tour routes that were output via the proposed algorithm had stronger spatial accessibility, and cost less than the sub-optimal ones, and were thus more environmentally friendly.

关键词： water-spot tourist spatial decision-making k-nn algorithm scenic water-spot classification spatial accessibility low-carbon and environmental protection

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