Random Forest Weighting based Feature Selection for C4.5 Algorithm on Wart Treatment Selection Method

Research in the field of health, especially treatment of wart disease has been widely practiced. One of the research topics related to the treatment of wart disease is in order to provide the most appropriate treatment method recommendations. Treatment methods are widely used by doctors for treatment of patients with wart disease that is the method of cryotherapy and immunotherapy. Previous research has been done on  cryotherapy and immunotherapy datasets which resulted in two different prediction methods, but the accuracy level has not been satisfactory. In this study, two datasets are combined to produce a single prediction method. The method uses C4.5 algorithm combined with  Random Forest Feature Weighting (C4.5+RFFW) used to select the relevant features to improve accuracy. Experimental results show that the proposed method can improve performance with accuracy and informedness are 87.22% and 71.24%, respectively. These results further facilitate physicians in determining treatment methods for patients with a single predictive method and better-predicted performance

Investigation of Factors Affecting Immunotherapy Treatment Results by Binary Logistic Regression and Classification Analysis

There are many factors that affect the success of immunotherapy treatment. In addition to the clinical examination, the investigation of these factors by different methods contributes to the researchers on prior knowledge and time. In this study, it was aimed to evaluate the application of logistic regression and data mining methods to evaluate the success of post-immunotherapy treatment. Bilateral logistic regression analysis with WTA and classification analysis with Weka were used to evaluate whether immunotherapy treatment was successful for warts. Decision tree structure is also discussed to determine the variables that affect classification success. According to the logistic regression result, the model is important because the probe. 0.022 <0.05. The classification result for the logistic regression model was calculated as 85.56%. This result shows that the model is successful. Data mining experiments were carried out with different classification algorithms. The best result was found in decision trees (with J48 algorithm) with 85.56% accuracy rate. According to the J48 algorithm decision tree structure, the variables that affect the outcome of the treatment were recorded as time, number of warts and age, respectively. Study results show that both methods yield parallel results. Decision tree algorithm is used as an alternative to classical statistical models. In particular, in cases where clinical research is limited, it will benefit researchers on topics such as transition to analysis, preliminary information gathering, time and effort

Position statement for the diagnosis and management of anogenital warts

Background: Anogenital warts (AGW) can cause economic burden on healthcare systems and are associated with emotional, psychological and physical issues. ----- Objective: To provide guidance to physicians on the diagnosis and management of AGW. ----- Methods: Fourteen global experts on AGW developed guidance on the diagnosis and management of AGW in an effort to unify international recommendations. Guidance was developed based on published international and national AGW guidelines and an evaluation of relevant literature published up to August 2016. Authors provided expert opinion based on their clinical experiences. ----- Results: A checklist for a patient's initial consultation is provided to help physicians when diagnosing AGW to get the relevant information from the patient in order to manage and treat the AGW effectively. A number of frequently asked questions are also provided to aid physicians when communicating with patients about AGW. Treatment of AGW should be individualized and selected based on the number, size, morphology, location, and keratinization of warts, and whether they are new or recurrent. Different techniques can be used to treat AGW including ablation, immunotherapy and other topical therapies. Combinations of these techniques are thought to be more effective at reducing AGW recurrence than monotherapy. A simplified algorithm was created suggesting patients with 1-5 warts should be treated with ablation followed by immunotherapy. Patients with >5 warts should use immunotherapy for 2 months followed by ablation and a second 2-month course of immunotherapy. Guidance for daily practice situations and the subsequent action that can be taken, as well as an algorithm for treatment of large warts, were also created. ----- Conclusion: The guidance provided will help physicians with the diagnosis and management of AGW in order to improve the health and quality of life of patients with AGW

Implementing decision tree-based algorithms in medical diagnostic decision support systems

As a branch of healthcare, medical diagnosis can be defined as finding the disease based on the signs and symptoms of the patient. To this end, the required information is gathered from different sources like physical examination, medical history and general information of the patient. Development of smart classification models for medical diagnosis is of great interest amongst the researchers. This is mainly owing to the fact that the machine learning and data mining algorithms are capable of detecting the hidden trends between features of a database. Hence, classifying the medical datasets using smart techniques paves the way to design more efficient medical diagnostic decision support systems. Several databases have been provided in the literature to investigate different aspects of diseases. As an alternative to the available diagnosis tools/methods, this research involves machine learning algorithms called Classification and Regression Tree (CART), Random Forest (RF) and Extremely Randomized Trees or Extra Trees (ET) for the development of classification models that can be implemented in computer-aided diagnosis systems. As a decision tree (DT), CART is fast to create, and it applies to both the quantitative and qualitative data. For classification problems, RF and ET employ a number of weak learners like CART to develop models for classification tasks. We employed Wisconsin Breast Cancer Database (WBCD), Z-Alizadeh Sani dataset for coronary artery disease (CAD) and the databanks gathered in Ghaem Hospital’s dermatology clinic for the response of patients having common and/or plantar warts to the cryotherapy and/or immunotherapy methods. To classify the breast cancer type based on the WBCD, the RF and ET methods were employed. It was found that the developed RF and ET models forecast the WBCD type with 100% accuracy in all cases. To choose the proper treatment approach for warts as well as the CAD diagnosis, the CART methodology was employed. The findings of the error analysis revealed that the proposed CART models for the applications of interest attain the highest precision and no literature model can rival it. The outcome of this study supports the idea that methods like CART, RF and ET not only improve the diagnosis precision, but also reduce the time and expense needed to reach a diagnosis. However, since these strategies are highly sensitive to the quality and quantity of the introduced data, more extensive databases with a greater number of independent parameters might be required for further practical implications of the developed models

Wart treatment method selection using AdaBoost with random forests as a weak learner

Selection of wart treatment method using machine learning is being a concern to researchers. Machine learning is expected to select the treatment of warts such as cryotherapy and immunotherapy to patients appropriately. In this study, the data used were cryotherapy and immunotherapy datasets. This study aims to improve the accuracy of wart treatment selection with machine learning. Previously, there are several algorithms have been proposed which were able to provide good accuracy in this case. However, the existing results still need improvement to achieve better level of accuracy so that treatment selection can satisfy the patients. The purpose of this study is to increase the accuracy by improving the performance of weak learner algorithm of ensemble machine learning. AdaBoost is used in this study as a strong learner and Random Forest (RF) is used as a weak learner. Furthermore, stratified 10-fold cross validation is used to evaluate the proposed algorithm. The experimental results show accuracy of 96.6% and 91.1% in cryotherapy and immunotherapy respectively

Early diagnosis and personalised treatment focusing on synthetic data modelling: Novel visual learning approach in healthcare

YesThe early diagnosis and personalised treatment of diseases are facilitated by machine learning. The quality of data has an impact on diagnosis because medical data are usually sparse, imbalanced, and contain irrelevant attributes, resulting in suboptimal diagnosis. To address the impacts of data challenges, improve resource allocation, and achieve better health outcomes, a novel visual learning approach is proposed. This study contributes to the visual learning approach by determining whether less or more synthetic data are required to improve the quality of a dataset, such as the number of observations and features, according to the intended personalised treatment and early diagnosis. In addition, numerous visualisation experiments are conducted, including using statistical characteristics, cumulative sums, histograms, correlation matrix, root mean square error, and principal component analysis in order to visualise both original and synthetic data to address the data challenges. Real medical datasets for cancer, heart disease, diabetes, cryotherapy and immunotherapy are selected as case studies. As a benchmark and point of classification comparison in terms of such as accuracy, sensitivity, and specificity, several models are implemented such as k-Nearest Neighbours and Random Forest. To simulate algorithm implementation and data, Generative Adversarial Network is used to create and manipulate synthetic data, whilst, Random Forest is implemented to classify the data. An amendable and adaptable system is constructed by combining Generative Adversarial Network and Random Forest models. The system model presents working steps, overview and flowchart. Experiments reveal that the majority of data-enhancement scenarios allow for the application of visual learning in the first stage of data analysis as a novel approach. To achieve meaningful adaptable synergy between appropriate quality data and optimal classification performance while maintaining statistical characteristics, visual learning provides researchers and practitioners with practical human-in-the-loop machine learning visualisation tools. Prior to implementing algorithms, the visual learning approach can be used to actualise early, and personalised diagnosis. For the immunotherapy data, the Random Forest performed best with precision, recall, f-measure, accuracy, sensitivity, and specificity of 81%, 82%, 81%, 88%, 95%, and 60%, as opposed to 91%, 96%, 93%, 93%, 96%, and 73% for synthetic data, respectively. Future studies might examine the optimal strategies to balance the quantity and quality of medical data

Improving Retrieval Performance of Case Based Reasoning Systems by Fuzzy Clustering

Case-based reasoning (CBR), which is a classical reasoning methodology, has been put to use. Its application has allowed significant progress in resolving problems related to the diagnosis, therapy, and prediction of diseases. However, this methodology has shown some complicated problems that must be resolved, including determining a representation form for the case (complexity, uncertainty, and vagueness of medical information), preventing the case base from the infinite growth of generated medical information and selecting the best retrieval technique. These limitations have pushed researchers to think about other ways of solving problems, and we are recently witnessing the integration of CBR with other techniques such as data mining. In this article, we develop a new approach integrating clustering (Fuzzy C-Means (FCM) and K-Means) in the CBR cycle. Clustering is one of the crucial challenges and has been successfully used in many areas to develop innate structures and hidden patterns for data grouping [1]. The objective of the proposed approach is to solve the limitations of CBR and improve it, particularly in the search for similar cases (retrieval step). The approach is tested with the publicly available immunotherapy dataset. The results of the experimentations show that the integration of the FCM algorithm in the retrieval step reduces the search space (the large volume of information), resolves the problem of the vagueness of medical information, speeds up the calculation and response time, and increases the search efficiency, which further improves the performance of the retrieval step and, consequently, the CBR system

Review of Immunotherapy Classification: Application Domains, Datasets, Algorithms and Software Tools from Machine Learning Perspective

YesImmunotherapy treatments can be essential sometimes and a waste of valuable resources in other cases, depending on the diagnosis results. Therefore, researchers in immunotherapy need to be updated on the current status of research by exploring: application domains e.g. warts, datasets e.g. immunotherapy, classifiers or algorithms e.g. kNN and software tools. The research objectives were: 1) to study the immunotherapy-related published literature from a supervised machine learning perspective. In addition, to reproduce immunotherapy classifiers reported in research papers. 2) To find gaps and challenges both in publications and practical work, which may be the basis for further research. Immunotherapy, diabetes, cryotherapy, exasens data and ”one unbalanced dataset” are explored. The results are compared with published literature. To address the found gaps in further research: novel experiments, unbalanced studies, focus on effectiveness and a new classifier algorithm are suggested

Colposcopy of the Vulva and Perineum

Due to the normal histology of this area and the multifocal nature of vulvar intraepithelial disease, vulvoscopy is more difficult and less objective than the cervix examination. Basis of vulvar colposcopy as well as benign vulvar skin disorders that are usually found in a routine gynecology examination will be reviewed