A Proposed Model for Lifestyle Disease Prediction Using Support Vector Machine

Patil, Mrunmayi; Lobo, Vivian Brian; Puranik, Pranav; Pawaskar, Aditi; Pai, Adarsh; Mishra, Rupesh Kumar

doi:10.1109/icccnt.2018.8493897

Cited by 24 publications

(4 citation statements)

References 7 publications

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“…These fora platforms serve as a great repository for generating profitable hypotheses for clinical analysis, using nothing other than data mining techniques. Data mining and other approaches could be used to amass valuable information via search logs, artificial intelligence-based chatbots, and even crowdsourcing [22].…”

Section: Convalescent Care Support and Involvementmentioning

confidence: 99%

Application of MLOps in Prediction of Lifestyle Diseases

et al. 2022

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Our health is the consequence of a combination of intricate and stabilized interactions between genetic elements, microbiome configuration, environmental impulses, and lifestyle habits. Knowledge of their contributions, as well as the complex network that connects them, is essential to analyze the underlying mechanisms and the onset of many diseases and can provide crucial information on their prevention, diagnosis, and remedy. We have seen advancements in data analytic methods to discover valuable patterns by analyzing great amounts of non-standard, heterogeneous, unstructured, and incomplete healthcare data. Not only does it help in decision making, but also forecasting. With the limited accuracy identified in machine learning models, we have implemented a process framework of Machine Learning Operations (MLOps) to develop a robust and collaborative platform for streamlining data and process integration and synergy through the automation of retaining, testing, and deployment.

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Section: Convalescent Care Support and Involvementmentioning

confidence: 99%

Application of MLOps in Prediction of Lifestyle Diseases

et al. 2022

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“…The result obtained shows that, the accuracy rate increased in some combination of algorithms. Patil et al (2018) proposed the model to analyze the lifecycle of the disease prediction using the ML algorithms. The authors used SVM to predict and analyze the lifecycle of the disease.…”

Section: Literature Surveymentioning

confidence: 99%

Monitoring and analysis of the recovery rate of Covid-19 positive cases to prevent dangerous stage using IoT and sensors

Kumar¹,

Iyapparaja

Niveditha

et al. 2020

IJPCC

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Purpose This paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease. Design/methodology/approach COVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world. Findings This proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate. Originality/value From that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.

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“…Mrunmayi et al constructed a model based on support vector machine to predict diseases. Support Vector Machine is suitable for dealing with high-dimensional data, but it exists the problem of high computational complexity, which makes the model obtain a poor accuracy [5]. Prof. Dhomse Kanchan et al adopted Random Forest, Support Vector Machine and Bayesian network to predict diabetes.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Cardiovascular Diseases based on Machine Learning

Sun

Zhang

Wang

et al. 2021

ASP trans. Internet things

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With the rapid development of artificial intelligence, it is very important to find the pattern of the data from the observed data and the functional dependency relationship between the data. By finding the existing functional dependencies, we can classify and predict them. At present, cardiovascular disease has become a major disease harmful to human health. As a disease with high mortality, the prediction problem of cardiovascular disease is becoming more and more urgent. However, some computer methods are mainly used for disease detection rather than prediction. If the computer method can be used to predict cardiovascular disease in advance and treat it as early as possible, then the consequences of the disease can be reduced to a certain extent. Diseases can be predicted by mechanical methods. Support vector machine (SVM) has strict mathematical theory support, and can deal with nonlinear classification after using kernel techniques. Therefore, support vector machine can be used to predict cardiovascular disease. On the other hand, we also use logical regression and random forest to predict cardiovascular disease. This paper mainly uses the method of machine learning to predict whether the population is sick or not. First of all, we preprocess the obtained data to improve the quality of the data, and then use svm and logical regression to predict, so as to provide reference for the prevention and treatment of cardiovascular diseases.

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A Proposed Model for Lifestyle Disease Prediction Using Support Vector Machine

Cited by 24 publications

References 7 publications

Application of MLOps in Prediction of Lifestyle Diseases

Application of MLOps in Prediction of Lifestyle Diseases

Monitoring and analysis of the recovery rate of Covid-19 positive cases to prevent dangerous stage using IoT and sensors

Prediction of Cardiovascular Diseases based on Machine Learning

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