Differential Diagnosis Of Erythemato-squamous Diseases Research Articles

Differential diagnosis of erythemato-squamous diseases using a hybrid ensemble machine learning technique

Erythemato-squamous Diseases (ESD) encompass a group of common skin conditions, including psoriasis, seborrheic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, and pityriasis rubra pilaris. These dermatological conditions affect a significant portion of the population and present a current challenge for accurate diagnosis and classification. Traditional classification methods struggle due to shared characteristics among these diseases. Machine Learning offers a valuable tool for aiding clinical decision-making in ESD classification. In this study, we leverage the UC Irvine (UCI) dermatology dataset by applying necessary preprocessing steps to handle missing data. We conduct a comparative analysis of two feature selection methods: One-way ANOVA and Chi-square test. To enhance the model’s performance, we employ hyper-parameter tuning through GridSearchCV. The training process encompasses various algorithms, including Support Vector Machine (SVM), Logistic Regression, k-Nearest Neighbors (kNN), and Decision Trees. The culmination of our work is a hybrid ensemble machine learning model that combines the strengths of the trained classifiers. This ensemble classifier achieves an impressive accuracy of 98.9% when validated using a 10-fold cross-validation approach.

Dermatolojik Verilerin Öz Düzenleyici Harita ve Destek Vektör Makinaları ile Sınıflandırılması

The frequency incidence of dermatological diseases is increasing in parallel with the fact that human skin is exposed to different chemicals. Examined many skin diseases, many of them are similar in shape and appearance, although the reasons for their appearance are different. In dermatology, the differential diagnosis of Erythemato-squamous diseases is frequently encountered by doctors. Doctors try to differentiate and diagnose diseases by evaluating clinical findings and histopathological parameters together. Many researchers have developed different algorithms on the classification and clustering of diseases and data that have been diagnosed from the UCI database. In the present study, unlike previous studies, clinical and histopathological findings of 6 different Erythamo Squamos skin diseases were clustered by applying to SOM network separately. As a result of this clustering process, it is determined that Psoriasis - Cronic Dermatitis and Seborreic Dermatitis - Pitriasis Rosea diseases were found in the same cluster and the diagnoses are confused. In order to prevent this confusion, clinical and histopathological findings of the diseases were clustered by SOM method. Clustering parameters of clinical and histopathological findings were classified with SVM. As a result of the study, it was achieved that the classification of Psoriasis - Cronic Dermatitis diseases was classified as 0.89 with an accuracy of 0.93 and that of Seborreic Dermatitis - Pitriasis Rosea with an accuracy of 0.79 and 0.80.

Fuzzy Neural System Application to Differential Diagnosis of Erythemato-Squamous Diseases

Fuzzy Neural System Application to Differential Diagnosis of Erythemato-Squamous Diseases

Random forests for automatic differential diagnosis of erythemato-squamous diseases

Erythemato–squamous diseases (ESD) are frequent skin diseases that share some clinical features of erythema and scaling. Their automatic diagnosis was tackled using several approaches that achieved high performance accuracy. However, they generally remained unattractive for dermatologists because of the lack of direct readability of their output models. Decision trees are easy to understand, but their performance and structure are very sensitive to data changes. Ensembles of decision trees were introduced to reduce the effect of these problems, but on the expense of interpretability. In this paper, we present the results of our investigation of random forests and boosting as ensemble methods for the differential diagnosis of ESD. Experiments on clinical and histopathological data showed that the random forest outperformed the other ensemble classifiers in terms of accuracy, sensitivity and specificity. Its diagnosis accuracy, attaining more than 98%, was also better than those of classifiers based on genetic programming, genetic algorithms and k–means clustering.

Genetic algorithm wrapped Bayesian network feature selection applied to differential diagnosis of erythemato-squamous diseases

This paper presents a new method for differential diagnosis of erythemato-squamous diseases based on Genetic Algorithm (GA) wrapped Bayesian Network (BN) Feature Selection (FS). With this aim, a GA based FS algorithm combined in parallel with a BN classifier is proposed. Basically, erythemato-squamous dataset contains six dermatological diseases defined with 34 features. In GA-BN algorithm, GA makes a heuristic search to find most relevant feature model that increase accuracy of BN algorithm with the use of a 10-fold cross-validation strategy. The subsets of features are sequentially used to identify six dermatological diseases via a BN fitting the corresponding data. The algorithm, in this case, produces 99.20% classification accuracy in the diagnosis of erythemato-squamous diseases. The strength of feature model generated for BN is furthermore tested with the use of Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Simple Logistics (SL) and Functional Decision Tree (FT). The resultant classification accuracies of algorithms are 98.36%, 97.00%, 98.36% and 97.81% respectively. On the other hand, BN algorithm with classification accuracy of 99.20% is quite a high diagnosis performance for erythemato-squamous diseases. The proposed algorithm makes no more than 3 misclassifications out of 366 instances. Furthermore, FS power of GA is also compared with two alternative search algorithms, i.e. Best First (BF) and Sequential Floating (SF). The obtained results have all together shown that the proposed GA-BN based FS and prediction strategy is very promising in diagnosis of erythemato-squamous diseases.

Instance-based classifiers applied to medical databases: Diagnosis and knowledge extraction

The aim of this paper is to study the feasibility and the performance of some classifier systems belonging to family of instance-based (IB) learning as second-opinion diagnostic tools and as tools for the knowledge extraction phase in the process of knowledge discovery in clinical databases. We consider three clinical databases: one relating to the differential diagnosis of erythemato-squamous diseases, the second to the diagnosis of the onset of diabetes mellitus and the third dealing with a problem of diagnostic imaging in nuclear cardiology. We apply five IB classifiers to each database; two are based on exemplars, one is based on prototypes and two are hybrid. One of the latter classifiers is a new classifier introduced here and is called prototype exemplar learning classifier (PEL-C). We use cross-validation techniques to evaluate and compare the performances of several classifier systems as diagnostic tools, considering indexes such as accuracy, sensitivity, specificity, and conciseness of class representations. Moreover we analyze the number and the type of instances that represent the diagnostic classes learnt by each classifier to evaluate and compare their knowledge extraction capabilities. An examination of the experimental results shows that classifiers with the best classification performances are the optimized k-nearest neighbour classifier (k-NNC) and PEL-C. The k-NNC uses the highest number of representative instances, 100% of the entire database, whereas PEL-C uses a far lesser number of representative instances: equal, on the average, to the 3% of the database. As tools for knowledge extraction, we interpret the kind of class representations obtained by IB classifiers as a form of nosological knowledge. Additionally, we report the most interesting diagnostic class representations to be those extracted by PEL-C because they are composed of a mixture of abstracted prototypical cases (syndromes) and selected atypical clinical cases. This study shows that IB methods - most notably, the optimized k-NNC and the PEL-C - can be used and may be advantageous for clinical decision support systems and that IB classifiers can be used for nosological knowledge extraction. Because PEL-C uses more compact and potentially meaningful class descriptions, it is preferable when the diagnostic problem at-hand needs smaller storage space or for knowledge extraction itself. The complexity and responsibility of diagnostic practice requires that these results be confirmed further within other clinical domains.

Erratum to: Automatic Detection of Erythemato-Squamous Diseases Using k-Means Clustering

Guvenir et al. [1] studied the classification of erythematosquamous diseases (psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis and pityriasis rubra pilaris). In dermatology departments the erythematosquamous diseases are often seen. The symptoms of these diseases are the clinical features of erythema and scaling and there are slight variations among the symptoms. Therefore, the differential diagnosis of erythematosquamous diseases is a difficult problem. As it is mentioned in the paper of Guvenir et al. [1], the patients were first evaluated clinically with 12 features. The important features in differential diagnosis are the degree of erythema and scaling, the borders of lesions, the presence of itching and koebner phenomenon, the formation of papules, the oral mucosa, elbows, knees and the scalp involvement and family history. According to Guvenir et al. [1] a successful diagnosis can be done with these clinical features only for some patients, but a biopsy is generally required for a definite and precise diagnosis. In their study, skin samples were used for the evaluation of 22 histopathological features. They noted that there is an obstacle for differential diagnosis due to the fact that a disease may have the histopathological features of another disease initially and may have the characteristic features further. For this reason, some samples represent the typical histopathological features of the disease, even though some of them do not. In this section, only a short description of diseases is presented and the readers should consult reference [1] for further details. In this study, we used the same dataset to apply the k-means classification method.

An ensemble of classifiers for the diagnosis of erythemato-squamous diseases

A new ensemble of support vector machines (SVM) based on random subspace (RS) and feature selection is developed and applied to the problem of differential diagnosis of erythemato-squamous diseases. Each classifier has a “favourite” class. To find the feature subset for the classifier D i with “favourite” class w i , we calculate the best features to discriminate this class ( w i ) from all the other classes. Our results improved the average predictive accuracy obtained by a “stand-alone” SVM or by a RS ensemble of SVM.

An expert system for the differential diagnosis of erythemato-squamous diseases

This paper presents an expert system for differential diagnosis of erythemato-squamous diseases incorporating decisions made by three classification algorithms: nearest neighbor classifier, naive Bayesian classifier and voting feature intervals-5. This tool enables doctors to differentiate six types of erythemato-squamous diseases using clinical and histopathological parameters obtained from a patient. The program also gives explanations for the classifications of each classifier. The patient records are also maintained in a database for further references.

Learning differential diagnosis of erythemato-squamous diseases using voting feature intervals

A new classification algorithm, called VFI5 (for Voting Feature Intervals), is developed and applied to problem of differential diagnosis of erythemato-squamous diseases. The domain contains records of patients with known diagnosis. Given a training set of such records, the VFI5 classifier learns how to differentiate a new case in the domain. VFI5 represents a concept in the form of feature intervals on each feature dimension separately. classification in the VFI5 algorithm is based on a real-valued voting. Each feature equally participates in the voting process and the class that receives the maximum amount of votes is declared to be the predicted class. The performance of the VFI5 classifier is evaluated empirically in terms of classification accuracy and running time.