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

Penerapan Particle Swarm Optimization pada Metode Neural Network untuk Perawatan Penyakit Kutil melalui Immunotherapy

Human papillomaviruses (HPVs) merupakan virus yang menimbulkan infeksi pada permukaan kulit dan dapt menyebabkan tumor sampai dengan kanker, salh satu penyakit yang disebabkan oleh HPVs adalah kutil. Immunotheraphy dapat dimanfaatkan untuk mengobati penyakit kutil. Sehingga penelitian ini melakukan penerapan metode neural network dengan algoritma PSO yang bertujuan untuk mengetahui nilai akurasi dari metode neural network dengan algoritma PSO yang berperan membantu menganalisis apakah peran immunotherapy lebih efektif dalam penyembuhan penyakit kutil dan kanker kulit. Setelah dilakukan pengujian melalui aplikasi rapid miner diketahui bahwa model Neural Network (NN) dengan algoritma PSO memiliki nilai akurasi sebesar 87.78%. Hasil perhitungan, performance keakurasian AUC yang diperoleh masuk kedalam kategori Good Classification dengan nilai AUC sebesar 0,757 dan memiliki nilai RMSE 0.331. Dengan demikian, metode Neural Network dengan algoritma PSO dapat digunakan untuk perawatan penyakit kutil melalui immunotherapy

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

Development and benchmarking a novel scatter search algorithm for learning probabilistic graphical models in healthcare

Healthcare data of small sizes are widespread, and the challenge of building accurate inference models is difficult. Many machine learning algorithms exist, but many are black boxes. Explainable models in healthcare are essential, so healthcare practitioners can understand the developed model and incorporate domain knowledge into the model. Probabilistic graphical models offer a visual way to represent relationships between data. Here we develop a new scatter search algorithm to learn Bayesian networks. This machine learning approach is applied to three case studies to understand the effectiveness in comparison with traditional machine learning techniques. First, a new scatter search approach is presented to construct the structure of a Bayesian network. Statistical tests are used to build small Directed acyclic graphs combined in an iterative process to build up multiple larger graphs. Probability distributions are fitted as the graphs are built up. These graphs are then scored based on classification performance. Once no new solutions can be found, the algorithm finishes. The first study looks at the effectiveness of the scatter search constructed Bayesian network against other machine learning algorithms in the same class. These algorithms are benchmarked against standard datasets from the UCI Machine Learning Repository, which has many published studies. The second study assesses the effectiveness of the scatter search Bayesian network for classifying ovarian cancer patients. Multiple other machine learning algorithms were applied alongside the Bayesian network. All data from this study were collected by clinicians from the Aneurin Bevan University Health Board. The study concluded that machine-learning techniques could be applied to classify patients based on early indicators. The third and final study looked into applying machine learning techniques to no-show breast cancer follow-up patients. Once again, the scatter search Bayesian network was used alongside other machine learning approaches. Socio-demographic and socio-economic factors involving low to middle-income families were used in this study with feature selection techniques to improve machine learning performance. It was found machine learning, when used with feature selection, could classify no-show patients with reasonable accuracy

Detecting adverse drug reactions in the general practice healthcare database

The novel contribution of this research is the development of a supervised algorithm that extracts relevant attributes from The Health Improvement Network database to detect prescription side effects. Prescription drug side effects are a common cause of morbidity throughout the world. Methods that aim to detect side effects have historically been limited due to the data available, but some of these limitations may be overcome by incorporating longitudinal observational databases into pharmacovigilance. Existing side effect detecting methods using longitudinal observational databases have shown promise at becoming a fundamental component of post marketing surveillance but unfortunately have high false positive rates. An extra step is required to further analyse and filter the potential side effects detected by existing methods due to their high false positive rates, and this reduces their efficiency. In this thesis a novel methodology, the supervised adverse drug reaction predictor (SAP) framework, is presented that learns from known side effects, and identifies patterns that can be utilised to detect unknown side effects. The Bradford-Hill causality considerations are used to derive suitable attributes as inputs into a learning algorithm. Both supervised and semi-supervised techniques are investigated due to the limited number of definitively known side effects. The results showed that the SAP framework implementing a random forest classifier outperformed the existing methods on The Health Improvement Network longitudinal observational database, with AUCs ranging between 0.812-0.937, an overall MAP of 0.667, precision values between 0.733-1 and a false positive rate ≤ 0.013. When applied to the standard reference the SAP framework implementing a support vector machine obtained a MAP score of 0.490, an average AUC of 0.703 and a false positive rate of 0.16. The false positive rate is lower than that obtained by existing methods on the standard reference

Detecting adverse drug reactions in the general practice healthcare database

The novel contribution of this research is the development of a supervised algorithm that extracts relevant attributes from The Health Improvement Network database to detect prescription side effects. Prescription drug side effects are a common cause of morbidity throughout the world. Methods that aim to detect side effects have historically been limited due to the data available, but some of these limitations may be overcome by incorporating longitudinal observational databases into pharmacovigilance. Existing side effect detecting methods using longitudinal observational databases have shown promise at becoming a fundamental component of post marketing surveillance but unfortunately have high false positive rates. An extra step is required to further analyse and filter the potential side effects detected by existing methods due to their high false positive rates, and this reduces their efficiency. In this thesis a novel methodology, the supervised adverse drug reaction predictor (SAP) framework, is presented that learns from known side effects, and identifies patterns that can be utilised to detect unknown side effects. The Bradford-Hill causality considerations are used to derive suitable attributes as inputs into a learning algorithm. Both supervised and semi-supervised techniques are investigated due to the limited number of definitively known side effects. The results showed that the SAP framework implementing a random forest classifier outperformed the existing methods on The Health Improvement Network longitudinal observational database, with AUCs ranging between 0.812-0.937, an overall MAP of 0.667, precision values between 0.733-1 and a false positive rate ≤ 0.013. When applied to the standard reference the SAP framework implementing a support vector machine obtained a MAP score of 0.490, an average AUC of 0.703 and a false positive rate of 0.16. The false positive rate is lower than that obtained by existing methods on the standard reference

Computer-assisted motion compensation and analysis of perfusion ultrasound data

Magdeburg, Univ., Fak. für Informatik, Diss., 2014von Sebastian Schäfe

Choose of Wart Treatment Method Using Naive Bayes and k-Nearest Neighbors Classifiers

26th IEEE Signal Processing and Communications Applications Conference (SIU) -- MAY 02-05, 2018 -- Izmir, TURKEYSarikaya, Mualla/0000-0001-8682-485X; Isler, Yalcin/0000-0002-2150-4756WOS:000511448500251In this study, the success of cyrotheraphy and immunotherapy methods on common warts and plantar warts were predicted among 180 patients using machine learning methods. As a classifier, Naive Bayes and k-nearest neighbors with different neighborhood values of k were experimented. Data sets that are online available via Internet were used in the study. As a result, whether the treatment method by considering given features will give positive result could be estimated with the accuracy of 80% by using k-nearest neighbors classifier with the neighborhood value of k=7.IEEE, Huawei, Aselsan, NETAS, IEEE Turkey Sect, IEEE Signal Proc Soc, IEEE Commun Soc, ViSRATEK, Adresgezgini, Rohde & Schwarz, Integrated Syst & Syst Design, Atilim Univ, Havelsan, Izmir Katip Celebi Uni