Prediction of hepatocellular carcinoma risk in patients with type-2 diabetes using supervised machine learning classification model

Azit, Noor Atika; Sahran, Shahnorbanun; Leow, Voon Meng; Subramaniam, Manisekar; Mokhtar, Suryati; Nawi, Azmawati Mohammed

doi:10.1016/j.heliyon.2022.e10772

Cited by 10 publications

(4 citation statements)

References 37 publications

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“…Azit et al [15] used SVM, artificial neural network, LR, and chi-square automatic interaction detection to predict diabetes.…”

Section: Literature Reviewmentioning

confidence: 99%

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A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction

Ajay Kumar,

Kamaldeep Kaur

2023

Int. j. eng. technol. innov.

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Early detection of diabetes is crucial because of its incurable nature. Several diabetes prediction models have been developed using machine learning techniques (MLTs). The performance of MLTs varies for different accuracy measures. Thus, selecting appropriate MLTs for diabetes prediction is challenging. This paper proposes a multi-criteria decision-making (MCDM) based framework for evaluating MLTs applied to diabetes prediction. Initially, three MCDM methods—WSM, TOPSIS, and VIKOR—are used to determine the individual ranks of MLTs for diabetes prediction performance by using various comparable performance measures (PMs). Next, a fusion approach is used to determine the final rank of the MLTs. The proposed method is validated by assessing the performance of 10 MLTs on the Pima Indian diabetes dataset using eight evaluation metrics for diabetes prediction. Based on the final MCDM rankings, logistic regression is recommended for diabetes prediction modeling.

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“…Azit et al [15] used SVM, artificial neural network, LR, and chi-square automatic interaction detection to predict diabetes.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This study used the PID dataset [16] that has been widely employed in previous studies [2,[7][8][9][10][13][14][15][16] for developing diabetes prediction models. This dataset was used to identify American Pima Indians with diabetes.…”

Section: Descriptionmentioning

confidence: 99%

A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction

Ajay Kumar,

Kamaldeep Kaur

2023

Int. j. eng. technol. innov.

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“…ML is a new discipline where computer science and statistics come together to solve problems [9], developing breakthroughs with the ability to detect and classify gaps in patient care [10,11]. The purpose of these ML models is to contribute to improving the quality of patient care and reducing medical costs [12,13]. This work is useful in determining the risk factors responsible for the development of diabetes from clinical data and predicting prediabetes.…”

Section: Introductionmentioning

confidence: 99%

Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes

Iparraguirre-Villanueva,

Espinola-Linares,

Flores Castañeda

et al. 2023

Diagnostics

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Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising.

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“…This is evident from the data, which predicts an increase in the number of individuals affected, projected to grow from 537 million in 2021 to 783 million by 2045 [1]. This ailment represents a substantial menace to the overall well-being of the global population [2]. Diabetes mellitus is a metabolic dysfunction that manifests when a person's body produces insufficient insulin, leading to exceptionally elevated blood glucose levels [3].…”

Section: Introductionmentioning

confidence: 99%

Implementation of LoRa Wireless Communication in Smart Diabetic Shoes Design

Purwono,

Burhan,

Lutviana

2023

IJRCS

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Diabetes is a widespread medical condition affecting a substantial portion of the global population. It is a metabolic disorder known as diabetes mellitus, characterized by severe fluctuations in blood glucose levels due to inadequate insulin production in the human body. The effective monitoring of diabetes is of paramount importance to researchers as it holds the potential to enhance the quality of healthcare services. Among the challenges faced by individuals with diabetes, one prevalent issue is the development of ulcers, which can be challenging to detect promptly. Technological advancements offer a promising avenue for cost-effective and continuous monitoring of chronic diseases like diabetes. This study centers on the development of IoT-based intelligent diabetic footwear that incorporates pressure sensors and temperature monitoring for individuals with diabetic feet. In the evaluation of SX1278 (Ra-02) Lora Module communication, deployed at eight different locations, it was observed that two of these points achieved a flawless transmission success rate of 100%. Conversely, the communication failed at points 6 and 7, with a success rate falling below 50%. Some of these failures can be attributed to signal obstructions, including natural elements such as trees and terrain, as well as man-made structures like buildings and machinery workshops, which hinder the efficient transmission of signals from the end node to the gateway. The results of this research provide positive implications in the form of developing IoT-based diabetes shoes that can be applied with alternative Lora communication connections in areas with poor internet signal.

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Prediction of hepatocellular carcinoma risk in patients with type-2 diabetes using supervised machine learning classification model

Cited by 10 publications

References 37 publications

A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction

A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction

Application of Machine Learning Models for Early Detection and Accurate Classification of Type 2 Diabetes

Implementation of LoRa Wireless Communication in Smart Diabetic Shoes Design

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