Diagnosis of diabetes type-II using hybrid machine learning based ensemble model

The Covid-19 Corona Virus, also known as SARS-CoV-2, has wreaked havoc around the world, and the condition is only getting worse.It is a pandemic disease spreading from person-to-person every day. Therefore, it is important to keep track the number of patients being affected. The current system gives the computerized data in a collective way which is very difficult to analyze and predict the growth of disease in a particular area and in the world. Machine learning algorithms can be used to successfully map the disease and its progression to solve this problem. Machine Learning, a branch of computer science, is critical in correctly distinguishing patients with the condition by analyzing their chest X-ray photographs. Supervised Machine learning models with associated algorithms (like LR, SVR and Time series algorithms) to analyze data for regression and classification helps in training the model to predict the number of total number of global confirmed cases who will be prone to the disease in the upcoming days. In this proposed work, the overall dataset of the world is being collected, preprocessed and the number of confirmed cases up to a particular date are extracted which is given as the training set to the model. The model is being trained by supervised machine learning algorithms to predict the growth of cases in the upcoming days. The experimental setup with the above mentioned algorithms shows that Time series Holt's model outperforms Linear Regression and Support Vector Regression algorithms.

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“…[1] , [2] , [3] , [4] , [5] , [6] , [8] , [9] , [10] , [12] , [14] , [17] , [18] , [19] , [22] , [23] , [24] , [25] .…”

Section: Uncited Referencesunclassified

Prediction of COVID-19 growth and trend using machine learning approach

Gothai¹,

Thamilselvan²,

Rajalaxmi³

et al. 2023

Materials Today: Proceedings

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“…Sarwar et el. [ 25 ] used ML for the diagnosis of diabetes, achieving 98% accuracy.These techniques can be fruitful for the detection and diagnosis of COVID-19. Firm and accurate analysis and diagnosis of COVID-19 can save thousands, if not millions, of lives while also producing large amounts of data that can be used to train and produce more robust ML models for the detection of this deadly virus.…”

Section: Literature Reviewmentioning

confidence: 99%

Future Forecasting of COVID-19: A Supervised Learning Approach

Rehman

Shafique

Khalid

et al. 2021

Sensors

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A little over a year after the official announcement from the WHO, the COVID-19 pandemic has led to dramatic consequences globally. Today, millions of doses of vaccines have already been administered in several countries. However, the positive effect of these vaccines will probably be seen later than expected. In these circumstances, the rapid diagnosis of COVID-19 still remains the only way to slow the spread of this virus. However, it is difficult to predict whether a person is infected or not by COVID-19 while relying only on apparent symptoms. In this context, we propose to use machine learning (ML) algorithms in order to diagnose COVID-19 infected patients more effectively. The proposed diagnosis method takes into consideration several symptoms, such as flu symptoms, throat pain, immunity status, diarrhea, voice type, body temperature, joint pain, dry cough, vomiting, breathing problems, headache, and chest pain. Based on these symptoms that are modelled as ML features, our proposed method is able to predict the probability of contamination with the COVID-19 virus. This method is evaluated using different experimental analysis metrics such as accuracy, precision, recall, and F1-score. The obtained experimental results have shown that the proposed method can predict the presence of COVID-19 with over 97% accuracy.

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“…The power spectrum and single-sided amplitude of respective signals are used to segregate the different stages. The optimised features are fed to Convolutional Neural Network (CNN) from which the values are learned by the neural network and it is classified into different stages like Normal, Mild, and Severe (Sarwar et al 2020). The result of the identification of diabetes showed that the ensemble methods ensured 98.60% accuracy.…”

Section: Literature Reviewsmentioning

confidence: 99%

Prediction of COVID-19 Patient using Supervised Machine Learning Algorithm

Buvana¹,

Muthumayil²

2021

JSM

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One of the most symptomatic diseases is COVID-19. Early and precise physiological measurement-based prediction of breathing will minimize the risk of COVID-19 by a reasonable distance from anyone; wearing a mask, cleanliness, medication, balanced diet, and if not well stay safe at home. To evaluate the collected datasets of COVID-19 prediction, five machine learning classifiers were used: Nave Bayes, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), and Decision Tree. COVID-19 datasets from the Repository were combined and re-examined to remove incomplete entries, and a total of 2500 cases were utilized in this study. Features of fever, body pain, runny nose, difficulty in breathing, shore throat, and nasal congestion, are considered to be the most important differences between patients who have COVID-19s and those who do not. We exhibit the prediction functionality of five machine learning classifiers. A publicly available data set was used to train and assess the model. With an overall accuracy of 99.88 percent, the ensemble model is performed commendably. When compared to the existing methods and studies, the proposed model is performed better. As a result, the model presented is trustworthy and can be used to screen COVID-19 patients timely, efficiently.

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Diagnosis of diabetes type-II using hybrid machine learning based ensemble model

Cited by 46 publications

References 16 publications

Prediction of COVID-19 growth and trend using machine learning approach

Prediction of COVID-19 growth and trend using machine learning approach

Future Forecasting of COVID-19: A Supervised Learning Approach

Prediction of COVID-19 Patient using Supervised Machine Learning Algorithm

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