Development of Rule-Based Diagnostic Algorithms with Artificial Intelligence Methods to Identify Papulosquamous Diseases

Papüloskuamöz deri hastalıkları halk arasında oldukça sık rastlanan ve kendine has morfolojik özellikleri olan deri hastalıkları grubudur. Papüloskuamöz deri hastalıklarının alt gruplarının belirtileri birbirine çok yakın olduğu için teşhis süreci bazı durumlarda zahmetlidir. Hastalığın teşhisi klinik muayenede konulabilir. Klinik muayenenin yetersiz olduğu durumlarda, tanı deri biyopsisi ile histopatolojik değerlendirme ile konulmaktadır. Bu süreçte dermatolog ve patoloğun uyumlu bir şekilde çalışması ve her iki hekimin de teşhis süreci ile ilgili bilgi birikiminin iyi olması gerekir. Bu yüzden Papüloskuamöz deri hastalıklarının tanısı deri biyopsisine ihtiyaç duyulmadan sadece klinik muayene ile dematolog tarafından konulabilmesi için daha basit, yüksek başarı oranına sahip ve klinikte kullanılabilir yöntemlere ihtiyaç duyulmaktadır. Bu çalışmanın amacı Papüloskuamöz deri hastalıklarının yüksek başarı oranı ile tespit edebilecek, klinikte dermatolog tarafından kullanılabilecek, yapay zeka yöntemleriyle geliştirilmiş kural tabanlı algoritma geliştirmektir. Çalışma kapsamında daha önce toplanmış veri seti kullanılmıştır. Veri setinde Papüloskuamöz deri hastalıklarının altı farklı alt grubu için klinik ve histopatolojik bulgular bulunmaktadır. Öncelikle veri seti ikişer sınıflı olacak şekilde gruplandırılmıştır. Daha sonra özellik seçme algoritmalarıyla klinik ve histopatolojik bulgular seçilmiştir. Daha sonra karar ağaçları yardımıyla kural tabanlı teşhis algoritmaları oluşturulmuştur. Çalışma sonucunda, sadece seçilmiş klinik bulgular kullanılarak ortalama %82.98 doğruluk oranı, 0.89 duyarlılık, 0.76 özgüllük oranıyla Papüloskuamöz deri hastalıkları kural tabanlı algoritmalar geliştirilmiştir. Sonuç olarak, bu çalışmada elde edilen sonuçlara göre, çalışma kapsamında geliştirilen algoritmalar, Papüloskuamöz deri hastalıklarının teşhisi için yapay zeka yöntemleriyle geliştirilen yüksek doğruluk oranına sahip kural tabanlı algoritmalar klinikte kullanılabilir.Papulosquamous skin diseases are common skin diseases and have morphological features. The diagnosis process_x000D_ is sometimes troublesome, as the symptoms of the subgroups of papulosquamous skin diseases are very close to_x000D_ each other. The diagnosis of the disease can be made at the clinical examination. In cases where the clinical_x000D_ examination is insufficient, the diagnosis is made by histopathological evaluation by skin biopsy. In this process,_x000D_ dermatologists and pathologists should work in harmony, and both doctors should have a good knowledge of the_x000D_ diagnosis process. Therefore, more uncomplicated, higher success rate, and clinically practical methods are needed_x000D_ in order for Papulosquamous skin diseases to be established only by a clinical examination by a dermatologist_x000D_ without the need for a skin biopsy. This study aims to develop a rule-based algorithm that can detect_x000D_ Papulosquamous skin diseases with a high success rate, can be used by dermatologists in the clinic, developed_x000D_ with artificial intelligence methods. Within the scope of the study, the previously collected data set was used. The_x000D_ data set contains clinical and histopathological findings for six different subgroups of Papulosquamous skin_x000D_ diseases. Firstly, the data set is grouped into two classes. Then, clinical and histopathological findings were_x000D_ selected with feature selection algorithms. Then, rule-based diagnostic algorithms were created with the help of_x000D_ decision trees. As a result of the study, Papulosquamous skin diseases rule-based algorithms have been developed_x000D_ with an average of 82.98% accuracy rate, 0.89 sensitivity, and 0.76 specificity rate using only selected clinical_x000D_ findings. Consequently, according to the results obtained in this study, algorithms developed within the scope of_x000D_ the study, high-accuracy rule-based algorithms developed with artificial intelligence methods can be used in the_x000D_ clinic for the diagnosis of Papulosquamous skin diseases.In job-shop production systems, orders are assigned to work centers according to their routes, and their operations are performed in this order. Production is becoming more and more complex with the increasing number of product lines and work centers with different routes. Decisions to be made according to the realtime monitoring of a dynamic production environment have become important. With the Fourth Industrial Revolution, information technologies are widely used in industries. A large amount of data is obtained from production tools that are capable of communicating with each other by means of Industry 4.0 and the internet of things. In this study, a simulation model of a production system that can collect data in real-time via sensors in work centers has been created and operation conditions have been determined. Then, work center / machine loading strategies were compared according to the delay periods of the jobs. The simulation model with the best loading strategy was run according to three different demand rates. Then data related with the delay status of the orders and the status of the work centers was obtained. The data were evaluated with data mining classification algorithms and rules were determined for delayed jobs. These rules were added to the simulation model as a decision mechanism. When an order is received in this model, the expert system estimates whether or not there will be a delay, and makes a decision to outsource the order’s production if needed. This approach further reduces the number of delayed order

Papüloskuamöz Hastalıkların Belirlenmesi için Yapay Zeka Yöntemleriyle Kural Tabanlı Teşhis Algoritmalarının Geliştirilmesi

Papüloskuamöz deri hastalıkları halk arasında oldukça sık rastlanan ve kendine has morfolojik özellikleri olan deri hastalıkları grubudur. Papüloskuamöz deri hastalıklarının alt gruplarının belirtileri birbirine çok yakın olduğu için teşhis süreci bazı durumlarda zahmetlidir. Hastalığın teşhisi klinik muayenede konulabilir. Klinik muayenenin yetersiz olduğu durumlarda, tanı deri biyopsisi ile histopatolojik değerlendirme ile konulmaktadır. Bu süreçte dermatolog ve patoloğun uyumlu bir şekilde çalışması ve her iki hekimin de teşhis süreci ile ilgili bilgi birikiminin iyi olması gerekir. Bu yüzden Papüloskuamöz deri hastalıklarının tanısı deri biyopsisine ihtiyaç duyulmadan sadece klinik muayene ile dematolog tarafından konulabilmesi için daha basit, yüksek başarı oranına sahip ve klinikte kullanılabilir yöntemlere ihtiyaç duyulmaktadır. Bu çalışmanın amacı Papüloskuamöz deri hastalıklarının yüksek başarı oranı ile tespit edebilecek, klinikte dermatolog tarafından kullanılabilecek, yapay zeka yöntemleriyle geliştirilmiş kural tabanlı algoritma geliştirmektir. Çalışma kapsamında daha önce toplanmış veri seti kullanılmıştır. Veri setinde Papüloskuamöz deri hastalıklarının altı farklı alt grubu için klinik ve histopatolojik bulgular bulunmaktadır. Öncelikle veri seti ikişer sınıflı olacak şekilde gruplandırılmıştır. Daha sonra özellik seçme algoritmalarıyla klinik ve histopatolojik bulgular seçilmiştir. Daha sonra karar ağaçları yardımıyla kural tabanlı teşhis algoritmaları oluşturulmuştur. Çalışma sonucunda, sadece seçilmiş klinik bulgular kullanılarak ortalama %82.98 doğruluk oranı, 0.89 duyarlılık, 0.76 özgüllük oranıyla Papüloskuamöz deri hastalıkları kural tabanlı algoritmalar geliştirilmiştir. Sonuç olarak, bu çalışmada elde edilen sonuçlara göre, çalışma kapsamında geliştirilen algoritmalar, Papüloskuamöz deri hastalıklarının teşhisi için yapay zeka yöntemleriyle geliştirilen yüksek doğruluk oranına sahip kural tabanlı algoritmalar klinikte kullanılabilir

An Enhanced Grey Wolf Optimization Based Feature Selection Wrapped Kernel Extreme Learning Machine for Medical Diagnosis

In this study, a new predictive framework is proposed by integrating an improved grey wolf optimization (IGWO) and kernel extreme learning machine (KELM), termed as IGWO-KELM, for medical diagnosis. The proposed IGWO feature selection approach is used for the purpose of finding the optimal feature subset for medical data. In the proposed approach, genetic algorithm (GA) was firstly adopted to generate the diversified initial positions, and then grey wolf optimization (GWO) was used to update the current positions of population in the discrete searching space, thus getting the optimal feature subset for the better classification purpose based on KELM. The proposed approach is compared against the original GA and GWO on the two common disease diagnosis problems in terms of a set of performance metrics, including classification accuracy, sensitivity, specificity, precision, G-mean, F-measure, and the size of selected features. The simulation results have proven the superiority of the proposed method over the other two competitive counterparts

Evolutionary computation-based feature selection for finding a stable set of features in high-dimensional data

Evolutionary Computation (EC) algorithms have proved to work well for feature selection because they are powerful search techniques and can produce multiple good solutions. However, they suffer from some limitations for real world applications. Firstly, ECs require high computation time as they evaluate many solutions at each iteration. Secondly, a classifier is usually used as their fitness function which causes the selected subset to perform well only on the utilised classifier (e.g. classifier-bias). Lastly, ECs, as stochastic search methods, return a different final subset in different runs which poses a problem for finding a stable set of features (e.g. stability issue). To address computation time and classifier-bias limitations, this thesis proposes a new two-stage selection approach called filter/filter in which two filter feature selection algorithms are combined. In the first stage, a ranking algorithm forms a reduced dataset by selecting the most informative features from the original dataset. In the second stage, the reduced dataset is fed to a novel EC algorithm to select final feature subset. This new EC algorithm is a Tabu search hybridised with an Asexual Genetic Algorithm called TAGA. TAGA benefits from new search components and solution representation which can effectively reduce computation time. To select a classifier-unbiased final subset, a statistical criterion is used as the fitness function which evaluates the subset independent of any classifier. Experiments show that the proposed filter/filter requires an acceptable computation time and selects more classifier-unbiased features compared to the state-of-the-arts. To find a stable set of features, a novel Generalisation Power Index (GPI) is proposed to analyse the generalisation power of final subsets of an EC in several runs. Generalisation power refers to performance capability of a subset over wide range of classifiers. Computation results confirm that GPI is able to find a stable set of features which achieves near optimal accuracy when used to train various classifiers. To ex amine the suitability of the proposed methods for real-world applications, the filter/filter approach and GPI are integrated to select a stable set of features for METABRIC breast cancer subtype classification problem. Experimental results show that this integration not only can address the limitations of ECs for a real-world biomedical feature selection problem but it performs better than alternatives methods

A New Feature Selection Method Based on Class Association Rule

Feature selection is a key process for supervised learning algorithms. It involves discarding irrelevant attributes from the training dataset from which the models are derived. One of the vital feature selection approaches is Filtering, which often uses mathematical models to compute the relevance for each feature in the training dataset and then sorts the features into descending order based on their computed scores. However, most Filtering methods face several challenges including, but not limited to, merely considering feature-class correlation when defining a feature’s relevance; additionally, not recommending which subset of features to retain. Leaving this decision to the end-user may be impractical for multiple reasons such as the experience required in the application domain, care, accuracy, and time. In this research, we propose a new hybrid Filtering method called Class Association Rule Filter (CARF) that deals with the aforementioned issues by identifying relevant features through the Class Association Rule Mining approach and then using these rules to define weights for the available features in the training dataset. More crucially, we propose a new procedure based on mutual information within the CARF method which suggests the subset of features to be retained by the end-user, hence reducing time and effort. Empirical evaluation using small, medium, and large datasets that belong to various dissimilar domains reveals that CARF was able to reduce the dimensionality of the search space when contrasted with other common Filtering methods. More importantly, the classification models devised by the different machine learning algorithms against the subsets of features selected by CARF were highly competitive in terms of various performance measures. These results indeed reflect the quality of the subsets of features selected by CARF and show the impact of the new cut-off procedure proposed

Enhancing Fuzzy Associative Rule Mining Approaches for Improving Prediction Accuracy. Integration of Fuzzy Clustering, Apriori and Multiple Support Approaches to Develop an Associative Classification Rule Base

Building an accurate and reliable model for prediction for different application domains, is one of the most significant challenges in knowledge discovery and data mining. This thesis focuses on building and enhancing a generic predictive model for estimating a future value by extracting association rules (knowledge) from a quantitative database. This model is applied to several data sets obtained from different benchmark problems, and the results are evaluated through extensive experimental tests. The thesis presents an incremental development process for the prediction model with three stages. Firstly, a Knowledge Discovery (KD) model is proposed by integrating Fuzzy C-Means (FCM) with Apriori approach to extract Fuzzy Association Rules (FARs) from a database for building a Knowledge Base (KB) to predict a future value. The KD model has been tested with two road-traffic data sets. Secondly, the initial model has been further developed by including a diversification method in order to improve a reliable FARs to find out the best and representative rules. The resulting Diverse Fuzzy Rule Base (DFRB) maintains high quality and diverse FARs offering a more reliable and generic model. The model uses FCM to transform quantitative data into fuzzy ones, while a Multiple Support Apriori (MSapriori) algorithm is adapted to extract the FARs from fuzzy data. The correlation values for these FARs are calculated, and an efficient orientation for filtering FARs is performed as a post-processing method. The FARs diversity is maintained through the clustering of FARs, based on the concept of the sharing function technique used in multi-objectives optimization. The best and the most diverse FARs are obtained as the DFRB to utilise within the Fuzzy Inference System (FIS) for prediction. The third stage of development proposes a hybrid prediction model called Fuzzy Associative Classification Rule Mining (FACRM) model. This model integrates the ii improved Gustafson-Kessel (G-K) algorithm, the proposed Fuzzy Associative Classification Rules (FACR) algorithm and the proposed diversification method. The improved G-K algorithm transforms quantitative data into fuzzy data, while the FACR generate significant rules (Fuzzy Classification Association Rules (FCARs)) by employing the improved multiple support threshold, associative classification and vertical scanning format approaches. These FCARs are then filtered by calculating the correlation value and the distance between them. The advantage of the proposed FACRM model is to build a generalized prediction model, able to deal with different application domains. The validation of the FACRM model is conducted using different benchmark data sets from the University of California, Irvine (UCI) of machine learning and KEEL (Knowledge Extraction based on Evolutionary Learning) repositories, and the results of the proposed FACRM are also compared with other existing prediction models. The experimental results show that the error rate and generalization performance of the proposed model is better in the majority of data sets with respect to the commonly used models. A new method for feature selection entitled Weighting Feature Selection (WFS) is also proposed. The WFS method aims to improve the performance of FACRM model. The prediction performance is improved by minimizing the prediction error and reducing the number of generated rules. The prediction results of FACRM by employing WFS have been compared with that of FACRM and Stepwise Regression (SR) models for different data sets. The performance analysis and comparative study show that the proposed prediction model provides an effective approach that can be used within a decision support system.Applied Science University (ASU) of Jorda

Artificial Intelligence in Oral Health

This Special Issue is intended to lay the foundation of AI applications focusing on oral health, including general dentistry, periodontology, implantology, oral surgery, oral radiology, orthodontics, and prosthodontics, among others

Parameter reduction in deep learning and classification

The goal of this thesis is to develop methods to reduce model and problem complexity in the area of classification tasks. Whether it is a traditional or a deep learning classification task, decreasing complexity helps to greatly improve efficiency, and also adds regularization to the models. In traditional machine learning, high-dimensionality can cause models to over-fit the training data, and hence not generalize well, while in deep learning, neural networks have shown to achieve state-of-the-art results, especially in the area of image recognition, in their current state cannot be easily deployed on memory restricted Internet-of-Things devices. Although much work has been carried out on dimensionality reduction, the first part of our work focuses on using dominancy between features in the aim to select a relevant subset of informative features. We propose 3 variations, with different benefits, including fast filter features selection and a hybrid filter-wrapper approach. In the second section, dedicated to deep learning, our work focuses on pruning methods to extract an overall much more efficient neural network. We show that our proposed techniques outperform previous state-of-the-art methods, across the different classification areas on a number of benchmark datasets using various classifiers and neural networks

Automated detection of depression from brain structural magnetic resonance imaging (sMRI) scans

 Automated sMRI-based depression detection system is developed whose components include acquisition and preprocessing, feature extraction, feature selection, and classification. The core focus of the research is on the establishment of a new feature selection algorithm that quantifies the most relevant brain volumetric feature for depression detection at an individual level