Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments

Shahid, Afzal Hussain; Singh, M. P.

doi:10.1016/j.bbe.2019.05.010

Cited by 42 publications

(21 citation statements)

References 284 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It was observed from the review of literature that many methods rely on the handcrafted features and traditional classifiers such as SVM, NB, k-NN, random forest, and fuzzy sets for diagnosis and prognosis of the diseases. Deep learning architectures were found to be an evident tool in the world of machine vision especially CNN classifiers which paved the way for more accurate results in medical imaging [67]. We obtained competitive results compared to the traditional classifiers and achieved 94.28% as accuracy rate in CNN (VGG16 net) classifier (deep learning) for distinguishing the normal from the diabetic subjects using tongue thermograms.…”

Section: Resultsmentioning

confidence: 93%

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus

Thirunavukkarasu

Snekhalatha

Krishnan

et al. 2020

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

Diabetes mellitus is one of the life threatening diseases over the globe, and an early prediction of diabetes is of utmost importance in this current scenario. International Diabetes Federation (IDF) reported nearly half of the world’s population was undiagnosed and unaware of being developed into diabetes. In 2017, around 84 million individuals were living with diabetes, and it might increase to 156 million by the end of 2045 stated by IDF. Generally, the diagnosis of diabetes relies on the biochemical method that may cause uneasiness and probability of infections to the subjects. To overcome such difficulties, a noninvasive method is much needed around the globe for primary screening. A change in body temperature is an indication of various diseases. Infrared thermal imaging is relatively a novel technique for skin temperature measurement and turned out to be well known in the medical field due to being noninvasive, risk-free, and repeatable. According to traditional Chinese medicine, the human tongue is a sensitive mirror that reflects the body’s pathophysiological condition. So, we have (i) analysed and classified diabetes based on thermal variations at human tongue, (ii) segmented the hot spot regions from tongue thermogram by RGB (red, green, blue) based color histogram image segmentation method and extracted the features using gray level co-occurrence matrix algorithm, (iii) classified normal and diabetes using various machine learning algorithms, and (iv) developed computer aided diagnostic system to classify diabetes mellitus. The baseline measurements and tongue thermograms were obtained from 140 subjects. The measured tongue surface temperature of the diabetic group was found to be greater than normal. The statistical correlation between the HbA1c and the thermal distribution in the tongue region was found to be r2 = 0.5688. The Convolutional Neural Network has outperformed the other classifiers with 94.28% accuracy rate. Thus, tongue thermograms could be used as a preliminary screening approach for diabetes prognosis.

show abstract

Section: Resultsmentioning

confidence: 93%

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus

Thirunavukkarasu

Snekhalatha

Krishnan

et al. 2020

Evidence-Based Complementary and Alternative Medicine

View full text Add to dashboard Cite

show abstract

“…Commonly used models in biomedical research include modified regression methods, such as lasso regression; k-nearest neighbors, which categorizes samples on the basis of their similarity to one another; support vector machines, which perform classification or regression by optimizing decision boundaries in multidimensional space; and decision tree algorithms, which create ensemble predictions from collections of individual decision trees. 11 Generally speaking, ANN approaches require less feature engineering than others, for instance, image-recognition models may require only basic cropping and color normalization. 7,11 ANNs, however, require more data to attain competitive performance and generally have higher computational demands than other models.…”

Section: Neural Networkmentioning

confidence: 99%

“…11 Generally speaking, ANN approaches require less feature engineering than others, for instance, image-recognition models may require only basic cropping and color normalization. 7,11 ANNs, however, require more data to attain competitive performance and generally have higher computational demands than other models. 12 In addition, non-ANN methods continue to demonstrate superior performance when handling relatively small collections of tabular data-that is, the kind of data, such as demographic and laboratory values, that could be summarized in a spreadsheet-an important consideration given the quantity of such data in health care.…”

Section: Neural Networkmentioning

confidence: 99%

Machine Learning in Oncology: What Should Clinicians Know?

Nagy

Radakovich

Nazha

2020

JCO Clinical Cancer Informatics

View full text Add to dashboard Cite

The volume and complexity of scientific and clinical data in oncology have grown markedly over recent years, including but not limited to the realms of electronic health data, radiographic and histologic data, and genomics. This growth holds promise for a deeper understanding of malignancy and, accordingly, more personalized and effective oncologic care. Such goals require, however, the development of new methods to fully make use of the wealth of available data. Improvements in computer processing power and algorithm development have positioned machine learning, a branch of artificial intelligence, to play a prominent role in oncology research and practice. This review provides an overview of the basics of machine learning and highlights current progress and challenges in applying this technology to cancer diagnosis, prognosis, and treatment recommendations, including a discussion of current takeaways for clinicians.

show abstract

“…Medical diagnosis is considered a difficult and complicated process even for clinicians due to the simultaneous consideration of several scenarios and factors related to medical evidence (Shahid and Singh, 2019). Diagnosis is the first step in medical practice, and it is crucial for clinical decision-making (Meena and Kumar, 2015).…”

Section: Introductionmentioning

confidence: 99%

“…Diagnosis is the first step in medical practice, and it is crucial for clinical decision-making (Meena and Kumar, 2015). In complex medical domains, conventional statistical techniques are not suitable to model complex medical domains, especially where both types of uncertainty (aleatoric and epistemic) are existing frequently (Shahid and Singh, 2019). Clinical decision systems have improved patient outcomes and cost of care (Berner and La, 2016); on the other hand, facing the complex uncertainty of human behavior as it is impractical to try to accurately characterize with a complete mathematical model.…”

Section: Introductionmentioning

confidence: 99%

Designing a medical rule model system by using rough–grey modeling

Karimi

Hojati

2020

View full text Add to dashboard Cite

PurposeIn this study, a hybrid rough and grey set-based rule model is designed for diagnosis of one type of blood cancer called multiple myeloma (MM). The grey clustering method is used to combine the same condition attributes and to improve the validity of the final model.Design/methodology/approachSome tools of the rough set theory (RST) and grey incidence analysis (GIA) are used in this research to analyze the serum protein electrophoresis (SPE) test results. An RST-based rule model is extracted based on the laboratory SPE test results of patients. Also, one decision attribute and 15 condition attributes are used to extract the rules. About four rule models are constructed due to the different algorithms of data complement, discretization, reduction and rule generation. In the following phases, the condition attributes are clustered into seven clusters by using a grey clustering method, the value set of the decision attribute is decreased by using manual discretizing and the number of observations is increased in order to improve the accuracy of the model. Cross-validation is used for evaluation of the model results and finally, the best model is chosen with 5,216 rules and 98% accuracy.FindingsIn this paper, a new rule model with high accuracy is extracted based on the combination of the grey clustering method and RST modeling for diagnosis of the MM disease. Also, four primary rule models and four improved rule models have been extracted from different decision tables in order to define the result of SPE test of patients. The maximum average accuracy of improved models is equal to 95% and related to the gamma globulins percentage attribute/object-related reducts (GA/ORR) model.Research limitations/implicationsThe total number of observations for rule extraction is 115 and the results can be improved by further samples. To make the designed expert system handy in the laboratory, new computer software is under construction to import data automatically from the electrophoresis machine into the resultant rule model system.Originality/valueThe main originality of this paper is to use the RST and GST together to design and create a hybrid rule model to diagnose MM. Although many studies have been carried out on designing expert systems in medicine and cancer diagnosis, no studies have been found in designing systems to diagnose MM. On the other hand, using the grey clustering method for combining the condition attributes is a novel solution for improving the accuracy of the rule model.

show abstract

Computational intelligence techniques for medical diagnosis and prognosis: Problems and current developments

Cited by 42 publications

References 284 publications

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus

Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus

Machine Learning in Oncology: What Should Clinicians Know?

Designing a medical rule model system by using rough–grey modeling

Contact Info

Product

Resources

About