Fuzzy method for pre-diagnosis of breast cancer from the Fine Needle Aspirate analysis

Clinical decision support systems for diabetic foot ulcers: a scoping review

OBJECTIVE

Map the scientific evidence on the use of clinical decision support systems in diabetic foot care.

METHOD

A scoping review based on the JBI Manual for Evidence Synthesis and registered on the Open Science Framework platform. Searches were carried out in primary and secondary sources on prototypes and computerized tools aimed at assisting patients with diabetic foot or at risk of having it, published in any language or period, in eleven databases and grey literature.

RESULTS

A total of 710 studies were identified and, following the eligibility criteria, 23 were selected, which portrayed the use of decision support systems in diabetic foot screening, predicting the risk of ulcers and amputations, classifying the stage of severity, deciding on the treatment plan, and evaluating the effectiveness of interventions, by processing data relating to clinical and sociodemographic information.

CONCLUSION

Expert systems stand out for their satisfactory results, with high precision and sensitivity when it comes to guiding and qualifying the decision-making process in diabetic foot prevention and care.

Adaptive fuzzy control of drug delivery in cancer treatment using combination of chemotherapy and antiangiogenic therapy

This paper introduces the adaptive fuzzy control scheme as a promising control technique for cancer treatment from a theoretical point of view. A mathematical model describing the dynamics of tumor growth under the drug interventions of chemotherapy and antiangiogenic therapy is considered. The model incorporates the effects of normal cells, cancer cells, and endothelial cells. Then, the control goals in cancer treatment are discussed and the desired trajectory for a typical patient is derived using the optimal control theory. Since the dynamic model of tumor growth is not accurate and also varies from patient to patient, an adaptive fuzzy controller is designed to make the outputs of the dynamic model track the desired trajectory. The proposed control system is model-independent and identifies the dynamic model of tumor growth over time. The performance of the designed controller is assessed by several numerical simulations. Finally, a hardware-in-the-loop simulation is conducted to validate the results.

Fuzzy Neural Network Expert System with an Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm for Early Diagnosis of Breast Cancer in Saudi Arabia

Breast cancer is one of the common malignancies among females in Saudi Arabia and has also been ranked as the one most prevalent and the number two killer disease in the country. However, the clinical diagnosis process of any disease such as breast cancer, coronary artery diseases, diabetes, COVID-19, among others, is often associated with uncertainty due to the complexity and fuzziness of the process. In this work, a fuzzy neural network expert system with an improved gini index random forest-based feature importance measure algorithm for early diagnosis of breast cancer in Saudi Arabia was proposed to address the uncertainty and ambiguity associated with the diagnosis of breast cancer and also the heavier burden on the overlay of the network nodes of the fuzzy neural network system that often happens due to insignificant features that are used to predict or diagnose the disease. An Improved Gini Index Random Forest-Based Feature Importance Measure Algorithm was used to select the five fittest features of the diagnostic wisconsin breast cancer database out of the 32 features of the dataset. The logistic regression, support vector machine, k-nearest neighbor, random forest, and gaussian naïve bayes learning algorithms were used to develop two sets of classification models. Hence, the classification models with full features (32) and models with the 5 fittest features. The two sets of classification models were evaluated, and the results of the evaluation were compared. The result of the comparison shows that the models with the selected fittest features outperformed their counterparts with full features in terms of accuracy, sensitivity, and sensitivity. Therefore, a fuzzy neural network based expert system was developed with the five selected fittest features and the system achieved 99.33% accuracy, 99.41% sensitivity, and 99.24% specificity. Moreover, based on the comparison of the system developed in this work against the previous works that used fuzzy neural network or other applied artificial intelligence techniques on the same dataset for diagnosis of breast cancer using the same dataset, the system stands to be the best in terms of accuracy, sensitivity, and specificity, respectively. The z test was also conducted, and the test result shows that there is significant accuracy achieved by the system for early diagnosis of breast cancer.

A Review of Computer-Aided Expert Systems for Breast Cancer Diagnosis

A computer-aided diagnosis (CAD) expert system is a powerful tool to efficiently assist a pathologist in achieving an early diagnosis of breast cancer. This process identifies the presence of cancer in breast tissue samples and the distinct type of cancer stages. In a standard CAD system, the main process involves image pre-processing, segmentation, feature extraction, feature selection, classification, and performance evaluation. In this review paper, we reviewed the existing state-of-the-art machine learning approaches applied at each stage involving conventional methods and deep learning methods, the comparisons within methods, and we provide technical details with advantages and disadvantages. The aims are to investigate the impact of CAD systems using histopathology images, investigate deep learning methods that outperform conventional methods, and provide a summary for future researchers to analyse and improve the existing techniques used. Lastly, we will discuss the research gaps of existing machine learning approaches for implementation and propose future direction guidelines for upcoming researchers.

Application of decision tree-based ensemble learning in the classification of breast cancer

As a common screening and diagnostic tool, Fine Needle Aspiration Biopsy (FNAB) of the suspicious breast lumps can be used to distinguish between malignant and benign breast cytology. In this study, we first review published works on the classification of breast cancer where the machine learning and data mining algorithms have been applied by using the Wisconsin Breast Cancer Database (WBCD). This work then introduces useful new tools, based on Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) algorithms to classify breast cancer. The RF and ET strategies use the decision trees as proper classifiers to attain the ultimate classification. The RF and ET approaches include four main stages: input identification, determination of the optimal number of trees, voting analysis, and final decision. The models implemented in this research consider important factors such as uniformity of cell size, bland chromatin, mitoses, and clump thickness as the input parameters. According to the statistical analysis, the proposed methods are able to classify the type of breast cancer accurately. The error analysis results reveal that the designed RF and ET models offer easy-to-use outcomes and the highest diagnostic performance, compared to previous tools/models in the literature for the WBCD classification. The highest and lowest magnitudes of relative importance are attributed to the uniformity of cell size and mitoses among the factors. It is expected that the RF and ET algorithms play an important role in medicine and health systems for screening and diagnosis in the near future.

Detection and classification of breast cancer using logistic regression feature selection and GMDH classifier

Breast cancer is the most common cancer among women such that the existence of a precise and reliable system for the diagnosis of benign or malignant tumors is critical. Nowadays, using the results of Fine Needle Aspiration (FNA) cytology and machine learning techniques, detection and early diagnosis of this cancer can be done with greater accuracy. In this paper, we propose a method consisting of two steps: in the first step, to eliminate the less important features, logistic regression has been used. In the second step, the Group Method Data Handling (GMDH) neural network is used for the diagnosis of benign and malignant samples. To evaluate the performance of the proposed method, three datasets WBCD, WDBC and WPBC are investigated with metrics: precision, the Area Under the ROC (AUC), true positive rate, false positive rate, accuracy and F-criteria. Simulation results show that the proposed method reaches a precision of 99.4% for WBCD, 99.6% for WDBC and a precision of 96.9% for WPBC dataset.

Fuzzy logic-based clinical decision support system for the evaluation of renal function in post-Transplant Patients

OBJECTIVES

In the context of the gradual development of artificial intelligence in health care, the clinical decision support systems (CDSS) play an increasing crucial role in improving the quality of the therapeutic and diagnostic efficiency in health care. The fuzzy logic (FL) provides an effective means for dealing with uncertainties in the health decision-making process; therefore, FL-based CDSS becomes a very powerful tool for data and knowledge management, being able to think like an expert clinician. This work proposes an FL-based CDSS for the evaluation of renal function in posttransplant patients.

METHOD

Based on the data provided by the Department of Nephrology of the University Hospital Federico II of Naples, a statistical sample is selected according to appropriate inclusion criteria. Four fuzzy inference systems are implemented monitoring the renal function by the level of proteinuria and the glomerular filtration rate (GFR).

RESULTS

The systems show an accuracy of more than 90% and the outputs are provided through easy to read graphics, so that physicians can intuitively monitor the patient's clinical status, with the objective to improve drugs dosage and reduce medication errors.

CONCLUSIONS

We propose that the CDSSs for the assessment and follow-up of kidney-transplanted patients built in this study are applicable to clinical practice.

Diagnostic methods and risk analysis based on fuzzy soft information

In our daily life problem we face uncertainties in making right decisions. In this study, we propose two different decision-making problems in medical field. The first problem is fever diagnosing and second problem is mouth cancer risk analysis. In the first problem, we use fuzzy soft similarity measures and fuzzy soft matrix operations to diagnose the type of fever. We consider a hypothetical case study and manipulate similarity measures on it. Our work diagnoses different patients having similar symptoms. We also develop a small application using JAVA. In the second problem, we perform risk analysis of mouth cancer. The proposed fuzzy soft expert system takes two biochemical parameters as inputs that is, serum total malondialdehyde (MDA), and serum proton donors capacity (donors_protons) and determines the risk of mouth cancer. Our study facilitates doctors by diagnosing mouth cancer at its earlier stages. There are four main components of our fuzzy soft expert system. The first component is named as fuzzification which converts crisp input into linguistic variables and formulates fuzzy sets. The second component transforms fuzzy sets into their respective fuzzy soft sets. The third component determines indispensable parameters and performs parameter reduction. The fourth component performs risk analysis by using algorithm. We use exemplary dataset and run all the components of fuzzy soft expert system to compute cancer risk.

Abnormalities of plantar pressure distribution in early, intermediate, and late stages of diabetic neuropathy

Inconsistent findings with regard to plantar pressure while walking in the diabetic population may be due to the heterogeneity of the studied groups resulting from the classification/grouping criteria adopted. The clinical diagnosis and classification of diabetes have inherent uncertainties that compromise the definition of its onset and the differentiation of its severity stages. A fuzzy system could improve the precision of the diagnosis and classification of diabetic neuropathy because it takes those uncertainties into account and combines different assessment methods. Here, we investigated how plantar pressure abnormalities evolve throughout different severity stages of diabetic polyneuropathy (absent, n=38; mild, n=20; moderate, n=47; severe, n=24). Pressure distribution was analysed over five areas while patients walked barefoot. Patients with mild neuropathy displayed an increase in pressure-time integral at the forefoot and a lower peak pressure at the heel. The peak and pressure-time integral under the forefoot and heel were aggravated in later stages of the disease (moderate and severe) compared with early stages of the disease (absent and mild). In the severe group, lower pressures at the lateral forefoot and hallux were observed, which could be related to symptoms that develop with the aggravation of neuropathy: atrophy of the intrinsic foot muscles, reduction of distal muscle activity, and joint stiffness. Although there were clear alterations over the forefoot and in a number of plantar areas with higher pressures within each severity stage, they did not follow the aggravation evolution of neuropathy classified by the fuzzy model. Based on these results, therapeutic interventions should begin in the early stages of this disease to prevent further consequences of the disease.