An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators

Chang, Victor; Ganatra, Meghana; Hall, Karl; Golightly, Lewis; Xu, Qianwen

doi:10.1016/j.health.2022.100118

Cited by 39 publications

(26 citation statements)

References 29 publications

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“…The predictive modeling results demonstrated that the random forest (RF) model outperformed other supervised models in accurately predicting incident CHD, which is in line with the ndings of similar studies [39,40].…”

Section: Optimal Machine Learning Modelsupporting

confidence: 80%

Unveiling Coronary Heart Disease Prediction through Machine Learning Techniques: Insights from the Suita Population-Based Cohort Study

Vu,

Kokubo,

Inoue

et al. 2024

Preprint

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We leveraged machine learning (ML) techniques, namely logistic regression (LR), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and LightGBM to predict coronary heart disease (CHD) and identify the key risk factors involved. Based on the Suita study, 7672 men and women aged 30 to 84 years without cardiovascular disease were recruited from 1989 to 1999, in Suita City, Osaka, Japan. Over an average period of 15 years, participants were diligently monitored until the onset of their initial cardiovascular event or relocation. CHD diagnoses encompassed primary heart attacks, sudden death, or coronary artery disease with bypass surgery or intervention. RF achieved the highest AUC (95% CI) of 0.79 (0.70–0.87), outperforming LR, SVM, XGBoost, and LightGBM. Shapley Additive Explanations (SHAP) on the best model identified the top CHD predictors. Notably, systolic blood pressure, non-HDL-c, glucose levels, age, metabolic syndrome, HDL-c, estimated glomerular filtration rate, hypertension, elbow joint thickness, and diastolic blood pressure were key contributors. Remarkably, elbow joint thickness was identified as a previously unrecognized risk factor associated with CHD. These findings indicated that ML methods accurately predict incident CHD risk. Additionally, ML has identified new incident CHD risk variables.

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Section: Optimal Machine Learning Modelsupporting

confidence: 80%

Unveiling Coronary Heart Disease Prediction through Machine Learning Techniques: Insights from the Suita Population-Based Cohort Study

Vu,

Kokubo,

Inoue

et al. 2024

Preprint

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“…Subsequently, erroneous data were eliminated by the utilization of K-means clustering. According to [18], the PIDD dataset was used to train seven distinct ML models, each with its own set of features. Two features were excluded in the feature selection process of this technique.…”

Section: Literature Reviewmentioning

confidence: 99%

SMOTE-based Deep LSTM System with GridSearchCV Optimization for Intelligent Diabetes Diagnosis

Smita Panigrahy

2024

jes

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Diabetes is a metabolic illness initiated by either inadequate insulin creation by the pancreas or the body's reduced responsiveness to insulin. It is characterised by consistently high levels of blood sugar and symptoms such as frequent urination, thirst, and increased appetite. Untreated diabetes can result in significant problems that impact crucial organs, presenting potentially fatal dangers. In order to address the need for accurate diagnosis, researchers have utilised artificial intelligence to develop the G-LSTM system. This system employs a novel method that combines SMOTE-based deep LSTM and GridSearchCV optimization to classify diabetes. This technique effectively tackles the issue of class imbalance in diabetes datasets, demonstrating an exceptional level of prediction accuracy. When tested on the PIMA dataset, G-LSTM demonstrated exceptional performance with an accuracy of 97.12%. Additionally, it produced high precision, recall, F1-score, AUC, and MCC values of 97.12%, 0.963, 0.954, 0.887, 0.989, and 0.882, respectively. The results highlight the higher performance of the G-LSTM method compared to other techniques, suggesting its use for clinical investigation of diabetes patients. This innovative intelligent diagnostic framework not only demonstrates the potential of artificial intelligence in healthcare, but also highlights its crucial role in enhancing the precision and effectiveness of diabetes diagnosis and treatment.

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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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An assessment of machine learning models and algorithms for early prediction and diagnosis of diabetes using health indicators

Cited by 39 publications

References 29 publications

Unveiling Coronary Heart Disease Prediction through Machine Learning Techniques: Insights from the Suita Population-Based Cohort Study

Unveiling Coronary Heart Disease Prediction through Machine Learning Techniques: Insights from the Suita Population-Based Cohort Study

SMOTE-based Deep LSTM System with GridSearchCV Optimization for Intelligent Diabetes Diagnosis

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

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