Cattle Disease Auxiliary Diagnosis and Treatment System Based on Data Analysis and Mining

Niu, Lijing; Yang, Changli; Du, Ying; Qin, Lihua; Li, Baoshan

doi:10.1109/icccs49078.2020.9118590

Cited by 5 publications

(2 citation statements)

References 10 publications

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“…The algorithm's name, which represents these crucial moments, derives from how the algorithm operates. A distinct difference between two groups is seen in Figure 1, demonstrating the effectiveness of SVM in defining discriminating decision boundaries for classification [18].…”

Section: Svmmentioning

confidence: 98%

Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning

Swain,

Pattnayak,

Mohanty

et al. 2024

IJEECS

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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 well-known machine learning models: Naïve Bayes multinomial (NBM), lazy-IBk, 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.

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Section: Svmmentioning

confidence: 98%

Smart livestock management: integrating IoT for cattle health diagnosis and disease prediction through machine learning

Swain,

Pattnayak,

Mohanty

et al. 2024

IJEECS

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“…As a result, the main goal of this paper is to analyze the data from numerous electronic medical records and classify sentences using machine learning's SVM method [5]. Then, using the data mining association algorithm, connect the cattle disease with the symptoms of the cow [6], and provide timely recommendations for diagnosis and treatment. 7) Syndrome differentiation (SD), a crucial step in TCM medicine for treating illnesses, can be viewed as a high-dimensional complex function in which symptoms and signs serve as the input and various syndrome types as the output [24].…”

Section: Literature Survey 1)mentioning

confidence: 99%

Cattle Disease Prediction Using Artificial Intelligence

Rao¹,

Monika²,

Rakshitha³

et al. 2023

IJRASET

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In today’s world identifying cattle disease and providing proper treatments is a challenging task in the current medical sector. As it is difficult to identify thecattle disease in real time, we require a method topredict cattle disease and related patterns. There are so many research works on this topic. Most of the researchworks just presented the idea of cattle disease prediction. There are many works where implementation is done and many papers predicts cattle disease using efficient data science algorithms. Research works where implementation is done uses PYTHON language or R language as programming language for cattle disease prediction. As PYTHON language and R language supports all ready libraries to process training datasets and to predict cattle disease. Many papers use training datasets from www.kaggle.com, www.dataworld.com etc.Research works uses efficient algorithms for prediction, algorithms such as Naive Bayes algorithm, KNNclassifier, SVM classifier, Decision Tree classifier, Random Forest algorithm etc. Most of the papers got very good results of using these algorithms. So many works on this cattle disease and pattern prediction is done using data science techniques.

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