[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123123-en":3,"doc-seo-123123-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123123,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","Smart Livestock Management - Integrating IoT for Cattle Health Diagnosis and Disease Prediction through Machine Learning","Timely detection of cattle diseases is essential to protect herd health and agricultural productivity, motivating machine learning–assisted diagnosis and prediction from measurable health parameters. The study collected 2,000 labeled samples covering conditions such as milk fever, diarrhea, vomiting, lameness, dehydration, and related abdominal and alkalosis issues. Five models—Naïve Bayes multinomial, lazyIBk, PART, random forest, and SVM—were evaluated to forecast disease presence or absence. Results show random forest achieved the best performance, reaching 88% accuracy on the test set, attributed to strong feature interaction handling and ensemble generalization that supports earlier risk identification.","Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning  \nSatyaprakash Swain1, Binod Kumar Pattnayak1, Mihir Narayan Mohanty2, Suvendra Kumar Jayasingh3, Kumar Janardan Patra3, Chittaranjan Panda4  \n1Department of Computer Science and Engineering, Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India  \n2Department of Electronics and Communication Engineering, Institute of Technical Education and Research (ITER), Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, India  \n3Department of Computer Science and Engineering, Institute of Management and Information Technology (IMIT), BPUT,  \nCuttack, India  \n4Mavenir Systems, Bengaluru, India  \n\n| Article history:\u003Cbr>Received Nov 24, 2023 Revised Dec 25, 2023 Accepted Dec 26, 2023 | Cattle diseases can significantly impact on livestock health and agricultural productivity is substantial. Timely detection and prognosis of these diseases are essential for prompt interventions and preventing their spread within the herd. This study delved into employing machine learning models to anticipate cattle diseases based on relevant parameters. These parameters encompass milk fever, milk clots, milk watery, milk flake, blisters, lameness, stomach pain, gaseous stomach, dehydration, diarrhea, vomiting, abdominal issues, and alkalosis. A dataset of 2,000 samples from diverse cattle populations was amassed, each tagged with the presence or absence of specific diseases. The primary goal was to compare the efficacy of five wellknown machine learning models: Naïve Bayes multinomial (NBM), lazyIBk, partial tree (PART), random forest (RF), and support vector machine (SVM) . The findings underscored the consistent superiority of RF in comparison to the other models, boasting the highest accuracy in predicting cattle diseases. The RF model exhibited an accuracy rate of 88% on the test dataset. This achievement can be ascribed to its capacity to handle intricate interactions among input features and mitigate over fitting through ensemble learning. These insights can furnish valuable information about early indicators and risk factors associated with diverse cattle diseases.\u003Cbr>This is an open access article under the CC BY-SA license.\u003Cbr> |\n| --- | --- |\n| Keywords:\u003Cbr>Boosting models Cattle health analysis Ensemble learning IoT\u003Cbr>Machine learning |  |\n\nCorresponding Author:  \nSatyaprakash Swain  \nDepartment of Computer Science and Engineering, Institute of Technical Education and Research (ITER) Siksha ‘O’ Anusandhan (Deemed to be University)  \nBhubaneswar, Odisha, India  \nEmail: [satyaimit@gmail.com](satyaimit@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nCattle health management holds a pivotal role in ensuring the livestock industry ’s well-being, productivity and profitability. The prompt identification and precise diagnosis of ailments or health issues in cattle are imperative for effective prevention and treatment. With the evolution of technology, machine learning methods have emerged as a potent instrument for medical prognosis and anticipation in various fields, including veterinary science. By harnessing machine learning algorithms and computational models, researchers and veterinarians can scrutinize extensive datasets to enhance the precision and efficiency of cattle health diagnosis. This manuscript presents an outline of the machine learning application in the realm  \nof cattle medical diagnosis and anticipation. It delves into the possible advantages, obstacles , and future potential of incorporating machine learning techniques into cattle healthcare frameworks. Machine learning algorithms possess the capability to process diverse data categories, ranging from clinical records , and laboratory test outcomes to genetic information and sensor data. This capacity facilitates the detection of disease patterns and markers, thereby permitting the ","cbCailzL7Y39Cr0A","https://ap.wps.com/l/cbCailzL7Y39Cr0A","pdf",660215,1,12,"English","en",105,"# Article Info Abstract\n## Introduction\n## Literature Review","[{\"question\":\"What problem does the document address in cattle farming?\",\"answer\":\"It focuses on improving timely detection and prognosis of cattle diseases to enable prompt interventions and prevent spread within the herd.\"},{\"question\":\"How was the machine learning evaluation conducted?\",\"answer\":\"A dataset of 2,000 labeled samples was used, and five models (NBM, lazyIBk, PART, random forest, SVM) were compared to predict disease presence or absence from health parameters.\"},{\"question\":\"Which model performed best and what accuracy did it achieve?\",\"answer\":\"Random forest (RF) consistently outperformed the other models, reaching 88% accuracy on the test dataset.\"}]","Smart Livestock Management - 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