Reinforcing Synthetic Data for Meticulous Survival Prediction of Patients Suffering From Left Ventricular Systolic Dysfunction

Khan, Mohammad Farhan; Gazara, Rajesh Kumar; Nofal, Muaffaq M.; Chakrabarty, Sohom; Dannoun, Elham M. A.; Al‐Hmouz, Rami; Mursaleen, M.

doi:10.1109/access.2021.3080617

Cited by 8 publications

(3 citation statements)

References 29 publications

(27 reference statements)

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“…Thanh Minh Vo, Tan Nhat Pham, and Son Vu Truong Dao [4] employed Gray Wolf Optimization and Adaptive Particle Swam Optimization to develop multilayer Perceptrons in order to detect diabetes. Gazara, Muaffaq M. Nofal, Sohom Chakrabarty, and M. Mursaleen [5] suggest a sophisticated pseudo-reinforcement learning method that overcomes the major class asymmetrical problem in a constricted dataset by incorporating simulated data into the major parameter space. A comprehensive framework for phenotyping biologic samples was devised by Mattia Delli Priscoli, Lisa Miccio, Francesco Bardozzo, and others [6].…”

Section: Literature Surveymentioning

confidence: 99%

A Survey on Identification and Diagnosis of Diseases Using Machine Learning

V.Madhumitha,

Dr.P.Thirumoorthy

2023

IJETMS

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The field of artificial intelligence to which machine learning belongs. We use machine learning methods like K-nearest neighbor(KNN), and Linear regression algorithm to detect and diagnose illnesses in this work. The dataset is trained using supervised learning, Reinforcement learning methods in order to construct a logical mathematical model. In the context of learning models, the datasets are employed for purposes such as data analysis and illness diagnosis. The purpose of the Disease Prediction using Machine Learning (ML) system is to make predictions about diseases based on the symptoms reported by patients or other users. The user inputs their symptoms, and the machine returns the likelihood that they have a certain ailment. In machine learning, disease prognosis relies on disease prediction.

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Section: Literature Surveymentioning

confidence: 99%

A Survey on Identification and Diagnosis of Diseases Using Machine Learning

V.Madhumitha,

Dr.P.Thirumoorthy

2023

IJETMS

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“…where, σ is a kernel scaling parameter. Substituting Equation (7) in Equation ( 6) defines the optimisation problem for the RBF kernel.…”

Section: Support Vector Machine (Svm) Modelmentioning

confidence: 99%

“…Hypertension is the physical exertion of the blood on the walls of the blood vessels, and is currently one of the major concern which is aggravating the risk of fatality through COVID-19 by approximately 250% [ 4 ]. Other than the COVID-19 risk factor, the prolonged uncontrolled hypertension above 140 systolic and 90 diastolic (in mmHg) can lead to the severe health risks such as cardiovascular disease and stroke [ 5 , 6 , 7 ]. The crucial pathway that holds the tendency to regulate blood pressure as well as systemic vascular resistance is the renin–angiotensin–aldosterone system (RAAS) [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Hypertension: Constraining the Expression of ACE-II by Adopting Optimal Macronutrients Diet Predicted via Support Vector Machine

et al. 2022

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The recent elevation of cases infected from novel COVID-19 has placed the human life in trepidation mode, especially for those suffering from comorbidities. Most of the studies in the last few months have undeniably raised concerns for hypertensive patients that face greater risk of fatality from COVID-19. Furthermore, one of the recent WHO reports has estimated a total of 1.13 billion people are at a risk of hypertension of which two-thirds live in low and middle income countries. The gradual escalation of the hypertension problem andthe sudden rise of COVID-19 cases have placed an increasingly higher number of human lives at risk in low and middle income countries. To lower the risk of hypertension, most physicians recommend drugs that have angiotensin-converting enzyme (ACE) inhibitors. However, prolonged use of such drugs is not recommended due to metabolic risks and the increase in the expression of ACE-II which could facilitate COVID-19 infection. In contrast, the intake of optimal macronutrients is one of the possible alternatives to naturally control hypertension. In the present study, a nontrivial feature selection and machine learning algorithm is adopted to intelligently predict the food-derived antihypertensive peptide. The proposed idea of the paper lies in reducing the computational power while retaining the performance of the support vector machine (SVM) by estimating the dominant pattern in the features space through feature filtering. The proposed feature filtering algorithm has reported a trade-off performance by reducing the chances of Type I error, which is desirable when recommending a dietary food to patients suffering from hypertension. The maximum achievable accuracy of the best performing SVM models through feature selection are 86.17% and 85.61%, respectively.

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