The paper presents a Multi-class Support Vector Machine classifier and its application to hypothyroid detection and classification. Support Vector Machines (SVM) have been well known method in the machine learning community for binary classification problems. Multi-class SVMs (MCSVM) are usually implemented by combining several binary SVMs. The objective of this work is to show: first, robustness of various kind of kernels for Multi-class SVM classifier, second, a comparison of different constructing methods for Multi-class SVM, such as One-Against-One and One-Against-All, and finally comparing the classifiers' accuracy of Multi-class SVM classifier to AdaBoost and Decision Tree. The simulation results show that One-Against-All Support Vector Machines (OAASVM) are superior to One-Against-One Support Vector Machines (OAOSVM) with polynomial kernels. The accuracy of OAASVM is also higher than AdaBoost and Decision Tree classifier on hypothyroid disease datasets from UCI machine learning dataset.

Multi-class Support Vector Machine (SVM) Classifiers -- An Application in Hypothyroid Detection and Classification

In this paper, we propose new methods for the choice and adaptation of the smoothing parameter of the probabilistic neural network (PNN). These methods are based on three reinforcement learning algorithms: $Q(0)$ -learning, $Q(\lambda )$ -learning, and stateless $Q$ -learning. We regard three types of PNN classifiers: the model that uses single smoothing parameter for the whole network, the model that utilizes single smoothing parameter for each data attribute, and the model that possesses the matrix of smoothing parameters different for each data variable and data class. Reinforcement learning is applied as the method of finding such a value of the smoothing parameter, which ensures the maximization of the prediction ability. PNN models with smoothing parameters computed according to the proposed algorithms are tested on eight databases by calculating the test error with the use of the cross validation procedure. The results are compared with state-of-the-art methods for PNN training published in the literature up to date and, additionally, with PNN whose sigma is determined by means of the conjugate gradient approach. The results demonstrate that the proposed approaches can be used as alternative PNN training procedures.

Application of Reinforcement Learning Algorithms for the Adaptive Computation of the Smoothing Parameter for Probabilistic Neural Network

Objective: This study aims to analyze the role of artificial neural networks (ANNs) in cytopathology. More specifically, it aims to highlight the importance of employing ANNs in existing and future applications and in identifying unexplored or poorly explored research topics.
Study design: A systematic search was conducted in scientific databases for articles related to cytopathology and ANNs with respect to anatomical places of the human body where cytopathology is performed. For each anatomic system/organ, the major outcomes described in the scientific literature are presented and the most important aspects are highlighted.
Results: The vast majority of ANN applications are related to cervical cytopathology, specifically for the ANN-based, semiautomated commercial diagnostic system PAPNET. For cervical cytopathology, there is a plethora of studies relevant to the diagnostic accuracy; in addition, there are also efforts evaluating cost-effectiveness and applications on primary, secondary, or hybrid screening. For the rest of the anatomical sites, such as the gastrointestinal system, thyroid gland, urinary tract, and breast, there are significantly less efforts relevant to the application of ANNs. Additionally, there are still anatomical systems for which ANNs have never been applied on their cytological material.
Conclusions: Cytopathology is an ideal discipline to apply ANNs. In general, diagnosis is performed by experts via the light microscope. However, this approach introduces subjectivity, because this is not a universal and objective measurement process. This has resulted in the existence of a gray zone between normal and pathological cases. From the analysis of related articles, it is obvious that there is a need to perform more thorough analyses, using extensive number of cases and particularly for the nonexplored organs. Efforts to apply such systems within the laboratory test environment are required for their future uptake.

https://journals.sagepub.com/doi/pdf/10.4137/BECB.S31601

Artificial Neural Networks as Decision Support Tools in Cytopathology: Past, Present, and Future

Thyroid disease is a major cause of formation in medical diagnosis and in theprediction, onset to which it is a difficult axiomin the medical research. Thyroid gland is one of the most important organs in our body. The secretions of thyroid hormones are culpable in controlling the metabolism. Hyperthyroidism and hypothyroidism are one of the two common diseases of the thyroid that releases thyroid hormones in regulating the rate of body's metabolism. Data cleansing techniques were applied to make the data primitive enough for performing analytics to show the risk of patients obtaining thyroid. The machine learning plays a decisive role in the process of disease prediction and this paper handles the analysis andclassificationmodels that are being used in the thyroid disease based on the information gathered from the dataset taken from UCI machine learning repository. It is important to ensure a decent knowledge base that can be entrenched and used as a hybrid model in solving complex learning task, such as in medical diagnosis and prognostic tasks. In this paper, we also proposed different machine learning techniques and diagnosis for the prevention of thyroid. Machine Learning Algorithms, support vector machine (SVM), K-NN, Decision Trees were used to predict the estimated risk on a patient's chance of obtaining thyroid disease.

Interactive Thyroid Disease Prediction System Using Machine Learning Technique

Classification procedure is an important task of expert and intelligent systems. Developing new algorithms of classification which improve accuracy or true positive rates could have an influence on some life problems such as diagnosis prediction in medical domain. Multi-criteria decision making (MCDM) methods are expected to search the best alternative according to some criteria. Each criterion has a value relative to each alternative. There are only two sets: a set of criteria and a set of alternatives. This work merges MCDM methods TOPSIS and VIKOR and modifies them to be used for classification where the used sets are three: the classes, the objects and the attributes (features) describing the objects. Hence, ATOVIC, a new classification algorithm is proposed. In ATOVIC, criteria are replaced by features and alternatives are replaced by objects. The latter belong to corresponding classes. Two sets are employed one serves as reference and second serves as test. An object from test set will be classified to the relative class based on the reference set. ATOVIC is applied on a benchmark (UCI) CLEVELAND data set to predict heart disease. Following the complexity of the data set and its importance, ATOVIC application is done on different test sets of CLEVELAND using binary classification and multi-classification. Moreover, ATOVIC is applied to thyroid data set to detect hyperthyroidism and hypothyroidism diseases. The obtained results show the efficiency of ATOVIC in medical domain. In addition, ATOVIC is applied to three other data sets: chess, nursery and titanic, from UCI and KEEL websites. The obtained results are compared to those of some classifiers from literature. The experimental results demonstrate that ATOVIC method improves accuracy and true positive rates comparing to most classifiers considered from literature. Hence ATOVIC is promising for use in prediction or classification.

Amended fused TOPSIS-VIKOR for classification (ATOVIC) applied to some UCI data sets

Thyroid gland produces thyroid hormones to help the regulation of the body's metabolism. The abnormalities of producing thyroid hormones are divided into two categories. Hypothyroidism which is related to production of insufficient thyroid hormone and hyperthyroidism related to production of excessive thyroid hormone. Separating these two diseases is very important for thyroid diagnosis. Therefore support vector machines and probabilistic neural network are proposed to classification. These methods rely mostly on powerful classification algorithms to deal with redundant and irrelevant features. In this paper feature selection is argued as an important problem via diagnosis and demonstrate that GAs provide a simple, general and powerful framework for selecting good subsets of features leading to improved diagnosis rates. Thyroid disease datasets are taken from UCI machine learning dataset.

Thyroid Disease Diagnosis Based on Genetic Algorithms Using PNN and SVM

Past work on face detection has emphasized the issues of feature extraction and classification, however, less attention has been given on the critical issue of feature selection. We consider the problem of face and non-face classification from frontal facial images using feature selection and neural networks. We argue that feature selection is an important issue in face and non-face classification. Automatic feature subset selection distinguishes the proposed method from previous face classification approaches. First, Principal Component Analysis (PCA) is used to represent each image as a feature vector (i.e., eigen-features) in a low-dimensional space, spanned by the eigenvectors of the covariance matrix of the training images (i.e., coefficients of the linear expansion).Then we consider Linear Discrimination Analysis (LDA) to achieve a comparison result between these two methods of dimension reduction. Genetic Algorithm (GA) is then used to select a subset of features from the low-dimensional representation by removing certain eigenvectors that do not seem to encode important information about face. Finally, a Probabilistic Neural Network (PNN) is trained to perform face classification using the selected eigen-feature subset. Experimental results demonstrate a significant improvement in error rate reduction.

Afsaneh Alavi Naini

Papers

Thyroid Disease Diagnosis Based on Genetic Algorithms Using PNN and SVM

Face detection based on dimension reduction using probabilistic neural network and Genetic Algorithm