Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms

Theerthagiri, Prasannavenkatesan; Ruby, A. Usha; Vidya, J

doi:10.1007/s42979-022-01485-3

Cited by 19 publications

(9 citation statements)

References 21 publications

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“…More tolerant to overfitting compared to a single decision tree XGBoost [63] max_depth: 1-10, gamma: [0, 0.4-1], min_child_weight: [1][2][3][4][5][6]8,10] Boosting for accuracy prediction C4.5 [206] Criterion: gini, max_depth: none, n_estimators: 150 Automatically identifying key risk factors associated with stroke CatBoost [10] learning_rate: 0.03, class_weight: 1, iterations: 100, depth: 6…”

Section: Strokementioning

confidence: 99%

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Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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According to the World Health Organization (WHO), some chronic diseases such as diabetes mellitus, stroke, cancer, cardiac vascular, kidney failure, and hypertension are essential for early prevention. One of the prevention that can be taken is to predict chronic diseases using machine learning based on personal medical record or general checkup result. The common prediction objective is to minimize the prediction error as low as possible. The most influencing chronic diseases prediction factors are the quality of data and the choice of predictor such as machine learning methods. The five main problems those lower data quality are outliers, missing values, feature selection, normalization, and imbalance. After we ensure the quality of data, the next task is to choose the best machine learning methods. The most influencing factor to consider when we choose the predictor its performance evaluation (accuracy, recall, precision, f1-score). Thus, predicting chronic disease aims to produce increased performance and solve problems in medical data. This paper presents a Systematic Literature Review (SLR) that offers a comprehensive discussion of research on chronic diseases prediction using machine learning and its data preprocessing handling. This paper covers machine learning methods discussion such as supervised learning, ensemble learning, deep learning, and reinforcement learning. The preprocessing handling we discuss includes missing values, outliers, feature selection, normalization, and imbalance. The final discussions of this paper are open issues, and the potential future works in improving the prediction performance for chronic diseases using a data preprocessing handling and machine learning methods.

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Section: Strokementioning

confidence: 99%

“…Machine learning can be used for data mining in the healthcare sector [4]. Applying machine learning in health data can help predict if a patient might have six chronic diseases: diabetes mellitus [5], [6]; cancer [7], [8]; stroke [9], [10]; hypertension [11], [12]; kidney failure [13], [14]; and heart issues [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Ghaniaviyanto Ramadhan,

Adiwijaya,

Maharani

et al. 2024

IEEE Access

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“…The second category includes interpretable models characterized by explicit prediction models. Most of these models rely on decision trees [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ]. Although the methods based on these trees [ 70 ] could provide explicit knowledge, in many cases, it is challenging to linearize the resulting acyclic decision graphs into simple decision rules.…”

Section: State Of the Artmentioning

confidence: 99%

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction

Falco

Cioppa

Koutný

et al. 2023

Sensors

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In this paper, we propose an innovative Federated Learning-inspired evolutionary framework. Its main novelty is that this is the first time that an Evolutionary Algorithm is employed on its own to directly perform Federated Learning activity. A further novelty resides in the fact that, differently from the other Federated Learning frameworks in the literature, ours can efficiently deal at the same time with two relevant issues in Machine Learning, i.e., data privacy and interpretability of the solutions. Our framework consists of a master/slave approach in which each slave contains local data, protecting sensible private data, and exploits an evolutionary algorithm to generate prediction models. The master shares through the slaves the locally learned models that emerge on each slave. Sharing these local models results in global models. Being that data privacy and interpretability are very significant in the medical domain, the algorithm is tested to forecast future glucose values for diabetic patients by exploiting a Grammatical Evolution algorithm. The effectiveness of this knowledge-sharing process is assessed experimentally by comparing the proposed framework with another where no exchange of local models occurs. The results show that the performance of the proposed approach is better and demonstrate the validity of its sharing process for the emergence of local models for personal diabetes management, usable as efficient global models. When further subjects not involved in the learning process are considered, the models discovered by our framework show higher generalization capability than those achieved without knowledge sharing: the improvement provided by knowledge sharing is equal to about 3.03% for precision, 1.56% for recall, 3.17% for F1, and 1.56% for accuracy. Moreover, statistical analysis reveals the statistical superiority of model exchange with respect to the case of no exchange taking place.

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“…In ( 21 ), researchers study delves into the comparative analysis of conventional classification techniques against neural network-driven machine learning approaches, specifically for a diabetes dataset. Furthermore, a plethora of performance metrics are assessed across multiple algorithms, such as K-nearest neighbor, Naive Bayes, extra trees, decision trees, radial basis function, and multilayer perceptron.…”

Section: Literature Reviewmentioning

confidence: 99%

An enhanced diabetes prediction amidst COVID-19 using ensemble models

Thakur,

Gera,

Bhardwaj

et al. 2023

Front. Public Health

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In the contemporary landscape of healthcare, the early and accurate prediction of diabetes has garnered paramount importance, especially in the wake of the COVID-19 pandemic where individuals with diabetes exhibit increased vulnerability. This research embarked on a mission to enhance diabetes prediction by employing state-of-the-art machine learning techniques. Initial evaluations highlighted the Support Vector Machines (SVM) classifier as a promising candidate with an accuracy of 76.62%. To further optimize predictions, the study delved into advanced feature engineering techniques, generating interaction and polynomial features that unearthed hidden patterns in the data. Subsequent correlation analyses, visualized through heatmaps, revealed significant correlations, especially with attributes like Glucose. By integrating the strengths of Decision Trees, Gradient Boosting, and SVM in an ensemble model, we achieved an accuracy of 93.2%, showcasing the potential of harmonizing diverse algorithms. This research offers a robust blueprint for diabetes prediction, holding profound implications for early diagnosis, personalized treatments, and preventive care in the context of global health challenges and with the goal of increasing life expectancy.

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Diagnosis and Classification of the Diabetes Using Machine Learning Algorithms

Cited by 19 publications

References 21 publications

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

Chronic Diseases Prediction Using Machine Learning With Data Preprocessing Handling: A Critical Review

A Federated Learning-Inspired Evolutionary Algorithm: Application to Glucose Prediction

An enhanced diabetes prediction amidst COVID-19 using ensemble models

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