A Non-Invasive Approach for Total Cholesterol Level Prediction Using Machine Learning

Garcia-D’Urso, Nahuel E.; Climent-Pérez, Pau; Sánchez‐SanSegundo, Miriam; Zaragoza-Martí, Ana; Fuster-Guilló, Andrés; Azorín-López, Jorge

doi:10.1109/access.2022.3178419

Cited by 13 publications

(4 citation statements)

References 20 publications

(12 reference statements)

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“…For purposes of feature extraction and classification, the actigraphy data of two days was ideal. Written by N. García-D'urso, P. Climent-Pérez et al [15], we introduced a machine learning method for predicting cholesterol levels from readily available and non-invasive data. utilised to access data transfer between patients, servers, and physicians.…”

Section: Literature Surveymentioning

confidence: 99%

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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%

“…Associating Measles Vaccine Uptake Classification and Its Underlying Factors Using an Ensemble of Machine Learning Models [7] Gaussian Using Machine Learning [15] Clustering technique sample size, and c) new variables, particularly those used in automated body composition analysis, can all help reduce the error rate.…”

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confidence: 99%

A Survey on Identification and Diagnosis of Diseases Using Machine Learning

V.Madhumitha,

Dr.P.Thirumoorthy

2023

IJETMS

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“…A machine learning approach for the prediction of cholesterol levels via regression using non-invasive and easy-to-collect data (clinical and anthropometric) is presented in [ 29 ]. In addition, clustering analysis is carried out to identify different groups of patients sharing some characteristics and give valuable information to clinical experts for diagnosis or prognosis.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction

Δρίτσας

Trigka

2022

Sensors

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Cholesterol is a waxy substance found in blood lipids. Its role in the human body is helpful in the process of producing new cells as long as it is at a healthy level. When cholesterol exceeds the permissible limits, it works the opposite, causing serious heart health problems. When a person has high cholesterol (hypercholesterolemia), the blood vessels are blocked by fats, and thus, circulation through the arteries becomes difficult. The heart does not receive the oxygen it needs, and the risk of heart attack increases. Nowadays, machine learning (ML) has gained special interest from physicians, medical centers and healthcare providers due to its key capabilities in health-related issues, such as risk prediction, prognosis, treatment and management of various conditions. In this article, a supervised ML methodology is outlined whose main objective is to create risk prediction tools with high efficiency for hypercholesterolemia occurrence. Specifically, a data understanding analysis is conducted to explore the features association and importance to hypercholesterolemia. These factors are utilized to train and test several ML models to find the most efficient for our purpose. For the evaluation of the ML models, precision, recall, accuracy, F-measure, and AUC metrics have been taken into consideration. The derived results highlighted Soft Voting with Rotation and Random Forest trees as base models, which achieved better performance in comparison to the other models with an AUC of 94.5%, precision of 92%, recall of 91.8%, F-measure of 91.7% and an accuracy equal to 91.75%.

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“…They used a machine learning approach to predict the cholesterol levels. Different groupings of patients are identified by using a clustering analysis [29].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Anthropometric Measurements Using Optimized Machine Learning Models with Bayesian Algorithm

Güleryüz

Efe

2023

Scientia Iranica

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This study collects the anthropometric measurements and weights of 185 male individuals between 55 and 65 years old from Ankara city of Turkey. A total of 29 variables with three inputs and twenty-six outputs are collected. This paper aims to develop machine learningbased models to estimate anthropometric measurements from weight, stature, and eye height. These models are support vector regression (SVR) optimized with Bayesian based on quadratic kernel, Gaussian Process Regression (GPR) optimized with Bayesian based on matern5/2 kernel. This study contributes to SVR and GPR models by using Bayesian method to optimize the parameters as a difference from the literature. According to the literature review, applying these two models to anthropometric measurements for the first time is predicted. The estimation results are compared based on three metrics, namely Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE). GPR optimized with Bayesian model has better accuracy than SVR optimized with Bayesian for all combinations except interpupillary distance, according to the obtained results. The RMSE values of the best models selected for each combination varied between 0.255 and 0.319 during the testing phase. Especially the estimations made with GPR optimized with Bayesian have a shallow error rate.

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A Non-Invasive Approach for Total Cholesterol Level Prediction Using Machine Learning

Cited by 13 publications

References 20 publications

A Survey on Identification and Diagnosis of Diseases Using Machine Learning

A Survey on Identification and Diagnosis of Diseases Using Machine Learning

Machine Learning Methods for Hypercholesterolemia Long-Term Risk Prediction

Estimation of Anthropometric Measurements Using Optimized Machine Learning Models with Bayesian Algorithm

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