Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective

Olisah, Chollette C.; Smith, Lyndon; Smith, Melvyn L.

doi:10.1016/j.cmpb.2022.106773

“…One of the most technical steps is hyperparameter tuning and optimization for the learning algorithm to improve the performance of ML models [52]. The common approaches to tuning the hyperparameters [53] of the models included grid search, repeated stratified k-fold cross-validation, genetic algorithms (GAs), random search [54], sequential search, the Gaussian process approach (GP), tree-structured Parzen estimator (TPE), and Bayesian optimization [55,56]. Recent research reported that model-based optimization (MBO) could be an excellent tuning strategy for RF.…”

Section: Analysis and Modellingmentioning

confidence: 99%

A review of radiomics and genomics applications in cancers: the way towards precision medicine

Li

¹

,

Zhou

²

2022

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The application of radiogenomics in oncology has great prospects in precision medicine. Radiogenomics combines large volumes of radiomic features from medical digital images, genetic data from high-throughput sequencing, and clinical-epidemiological data into mathematical modelling. The amalgamation of radiomics and genomics provides an approach to better study the molecular mechanism of tumour pathogenesis, as well as new evidence-supporting strategies to identify the characteristics of cancer patients, make clinical decisions by predicting prognosis, and improve the development of individualized treatment guidance. In this review, we summarized recent research on radiogenomics applications in solid cancers and presented the challenges impeding the adoption of radiomics in clinical practice. More standard guidelines are required to normalize radiomics into reproducible and convincible analyses and develop it as a mature field.

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“…Data pre-processing enables the generation of a robust classification model with high accuracy [22]. At this stage, some initial operations are performed on the PIDD to improve classification precision.…”

Section: Pre-processing Stagementioning

confidence: 99%

A proposed approach for diabetes diagnosis using neuro-fuzzy technique

Alasaady

¹

,

Aris

²

,

Sharef

³

et al. 2022

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Diabetes is a chronic disease characterized by a decrease in pancreatic insulin production. The immune system will be harmed due to this condition, which will raise blood sugar levels. However, early detection of diabetes enables patients to begin treatment on time, therefore reducing or eliminating the risk of severe consequences. One of the most significant challenges in the healthcare unit is disease diagnosis. Traditional techniques of disease diagnosis are manual and prone to inaccuracy. This paper proposed an approach for diagnosing diabetes using the adaptive neuro-fuzzy inference system (ANFIS) based on Pima Indians diabetes dataset (PIDD). The three stages of the proposed approach are pre-processing classification and evaluation. Normalization, imputation, and anomaly detection are part of the pre-processing stage. The pre-processing was done by normalizing the data, replacing the missing values, and using the local outlier factor (LOF) technique. In the classification stage, ANFIS classifiers were trained using the hybrid learning algorithm of the neural network. Finally, the evaluation procedures use the last stage’s sensitivity, specificity, and accuracy metrics. The obtained classification accuracy was 92.77%, and it seemed rather promising compared to the other classification applications for this topic found in the literature.

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“…other parameter adjustment methods [28]. SVM is usually used as control algorithm in diabetic prediction [29], while our model has obtained the optimal parameters effectively considering the key parameters as well as their large space and enhanced the prediction precision, where we discussed its parameter tuning and provided a new conception of parameter adjustment.…”

Section: Train[-validationidx] Validation � Dtrain[validationidx]mentioning

confidence: 99%

Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors

Yang

¹

,

Li

²

,

Lin

³

et al. 2022

Journal of Healthcare Engineering

0

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Objective. Immune checkpoint inhibitors, such as programmed death-1/ligand-1 (PD-1/L1), exhibited autoimmune-like disorders, and hyperglycemia was on the top of grade 3 or higher immune-related adverse events. Machine learning is a model from past data for future data prediction. From post-marketing monitoring, we aimed to construct a machine learning algorithm to efficiently and rapidly predict hyperglycemic adverse reaction in patients using PD-1/L1 inhibitors. Methods. In original data downloaded from Food and Drug Administration Adverse Event Reporting System (US FAERS), a multivariate pattern classification of support vector machine (SVM) was used to construct a classifier to separate adverse hyperglycemic reaction patients. With correct core SVM function, a 10-fold 3-time cross validation optimized parameter value composition in model setup with R language software. Results. The SVM prediction model was set up from the number type/number optimization method, as well as the kernel and type of “rbf” and “nu-regression” composition. Two key values (nu and gamma) and case number displayed high adjusted r2 in curve regressions ( n u = 0.5649 × e − case / 6984 , gamma = 9.005 × 10 − 4 × case − 4.877 × 10 − 8 × case 2 ). This SVM model with computable parameters greatly improved the assessing indexes (accuracy, F1 score, and kappa) as well as coequal sensitivity and the area under the curve (AUC). Conclusion. We constructed an effective machine learning model based on compositions of exact kernels and computable parameters; the SVM prediction model can noninvasively and precisely predict hyperglycemic adverse drug reaction (ADR) in patients treated with PD-1/L1 inhibitors, which could greatly help clinical practitioners to identify high-risk patients and perform preventive measurements in time. Besides, this model setup process provided an analytic conception for promotion to other ADR prediction, such ADR information is vital for outcome improvement by identifying high-risk patients, and this machine learning algorithm can eventually add value to clinical decision making.

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Diabetes mellitus prediction and diagnosis from a data preprocessing and machine learning perspective

Cited by 95 publications

References 24 publications

A review of radiomics and genomics applications in cancers: the way towards precision medicine

A review of radiomics and genomics applications in cancers: the way towards precision medicine

A proposed approach for diabetes diagnosis using neuro-fuzzy technique

Machine Learning for Predicting Hyperglycemic Cases Induced by PD-1/PD-L1 Inhibitors

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