Mortality Prediction of Various Cancer Patients via Relevant Feature Analysis and Machine Learning

Breast, lung, prostate, and stomach cancers are the most frequent cancer types globally. Early-stage detection and diagnosis of these cancers pose a challenge in the literature. When dealing with cancer patients, physicians must select among various treatment methods that have a risk factor. Since the risks of treatment may outweigh the benefits, treatment schedule is critical in clinical decision making. Manually deciding which medications and treatments are going to be successful takes a lot of expertise and can be hard. In this paper, we offer a computational solution to predict the mortality of various types of cancer patients. The solution is based on the analysis of diagnosis, medication, and treatment parameters that can be easily acquired from electronic healthcare systems. A classification-based approach introduced to predict the mortality outcome of cancer patients. Several classifiers evaluated on the Medical Information Mart in Intensive Care IV (MIMIC-IV) dataset. Diagnosis, medication, and treatment features extracted for breast, lung, prostate, and stomach cancer patients and relevant feature selection done with Logistic Regression. Best F1 scores were 0.74 for breast, 0.73 for lung, 0.82 for prostate, and 0.79 for stomach cancer. Best AUROC scores were 0.94 for breast, 0.91 for lung, 0.96 for prostate, and 0.88 for stomach cancer. In addition, using relevant features, results were very similar to the baseline for each cancer type. Using less features and a robust machine-learning model, the proposed approach can be easily implemented in hospitals when there are limited data and resources available.publishedVersionPeer reviewe

A deep learning approach for parkinson’s disease severity assessment

Purpose: Parkinson’s Disease comes on top among neurodegenerative diseases affecting 10 million worldwide. To detect Parkinson’s Disease in a prior state, gait analysis is an effective choice. However, monitoring of Parkinson’s Disease using gait analysis is time consuming and exhaustive for patients and physicians. To assess severity of symptoms, a rating scale called Unified Parkinson's Disease Rating Scale is used. It determines mild and severe cases. Today, Parkinson’s Disease severity assessment is made in gait laboratories and by manual examination. These are time consuming and it is costly for health institutions to build and maintain laboratories. By using low-cost wearables and an effective model, aforementioned problems can be solved. Methods: We provide a computerized solution for quantifiable assessment of Parkinson’s Disease symptoms severity. By using wearable sensors, our framework can predict exact symptom values to assess Parkinson’s Disease severity. We propose a deep learning approach that utilizes Ground Reaction Force sensors. From sensor signals, features are extracted and fed to a hybrid deep learning model. This model is the combination of Convolutional Neural Networks and Locally Weighted Random Forest. Results: Proposed framework achieved 0.897, 3.009, 4.556 in terms of Correlation Coefficient, Mean Absolute Error and Root Mean Square Error, respectively. Proposed framework outperformed other machine and deep learning models. We also evaluated classification performance for disease detection. We outperformed most of the previous studies, achieving 99.5% accuracy, 98.7% sensitivity and 99.1% specificity. Conclusion: This is the first study to use a deep learning regression approach to predict exact symptom value of Parkinson’s Disease patients. Results show that this approach can be effectively employed as a disease severity assessment tool using wearable sensors.publishedVersionPeer reviewe

Predicting infections using computational intelligence – A systematic review

Infections encompass a set of medical conditions of very diverse kinds that can pose a significant risk to health, and even death. As with many other diseases, early diagnosis can help to provide patients with proper care to minimize the damage produced by the disease, or to isolate them to avoid the risk of spread. In this context, computational intelligence can be useful to predict the risk of infection in patients, raising early alarms that can aid medical teams to respond as quick as possible. In this paper, we survey the state of the art on infection prediction using computer science by means of a systematic literature review. The objective is to find papers where computational intelligence is used to predict infections in patients using physiological data as features. We have posed one major research question along with nine specific subquestions. The whole review process is thoroughly described, and eight databases are considered which index most of the literature published in different scholarly formats. A total of 101 relevant documents have been found in the period comprised between 2003 and 2019, and a detailed study of these documents is carried out to classify the works and answer the research questions posed, resulting to our best knowledge in the most comprehensive study of its kind. We conclude that the most widely addressed infection is by far sepsis, followed by Clostridium difficile infection and surgical site infections. Most works use machine learning techniques, from which logistic regression, support vector machines, random forest and naive Bayes are the most common. Some machine learning works provide some ideas on the problems of small data and class imbalance, which can be of interest. The current systematic literature review shows that automatic diagnosis of infectious diseases using computational intelligence is well documented in the medical literature.publishedVersio

Hitit çiviyazısı işaretlerinin bilgisayar desteği ile okunması ve veri madenciliği uygulama örnekleri

Anadolu’da M.Ö. 1650 - 1200 yılları boyunca hüküm süren Hitit krallığı ve imparatorluğu o dönem dünyasının en büyük güçlerinden birisi sayılmaktadır. Hititlerin kullandığı Hititçe, Hint-Avrupa dil ailesinin bilinen en eski üyelerinden biridir. Hititler dünyada arşiv-kütüphane uygulamasını ortaya koyan ilk toplumlardan biridir. Hititler çeşitli konulardaki metinleri Hitit çiviyazısı ile yaş kil tabletler üstüne yazıp tabletleri çoğunlukla fırınlayarak kalıcı hale getirmişlerdir. Hitit çiviyazılı metinlerin okunması, çevrilmesi, yorumlanması ve gramer kurallarının kullanımı yaklaşık yüz yıldır “ele ve insana” dayalı olarak yapılan, uzun süre ve emek isteyen, yorucu bir uğraştır. Anadolu’da yerüstünde ve hâlâ yeraltında bulunan kil tabletler üstündeki çiviyazısı işaretlerini günümüz bilgi ve bilgisayar destekli tekniklerle okuyabilmek sadece Anadolu değil tüm insanlık tarihi ve kültürü açısından son derece önemlidir. Bu çalışmada, imge işleme yöntemleri ile Hitit çiviyazılı tabletlerde bulunan çiviyazısı işaretlerinin okunması gerçekleştirilmiştir. Ayrıca çalışmada veri madenciliği teknikleri kullanılarak çiviyazılı işaretlerin sahip olduğu geometrik özelliklere göre sınıflara ayrılması ile ilgili uygulama örneklerine de yer verilmiştir. In Anatolia the kingdom and empire of the Hittites had ruled nearly half a millenium during the years BC 1650-1200. It was considered one of the greatest world power of that time. Hittite language that the Hittites used is one of the oldest member of the Indo-European language family. The Hittites were one of the first communities that had adapted the concept of archive-library. The Hittites used cuneiform signs to write on various topics on wet clay tablets and baked them to be permanent and durable. The study of Hittite language grammar rules followed transliteration, transcription and translation phases manually on the Hittite cuneiform tablets. It takes a long time, it requires financial support and a special know-how and expertise for processing. It is a tedious job. Many more tablets are still waiting under and over ground to be read and translated. Being able to read the signs on cuneiform clay tablets still in Anatolia, using computer-aided techniques would be a significant contribution not only to Anatolian but also to human history. In this study, recognition of Hittite cuneiform signs is performed by using image processing techniques. Also in this study; using data mining, applications related to classification of Hittite cuneiform signs based on their geometrical features are performed

T4SS Effector Protein Prediction with Deep Learning

Extensive research has been carried out on bacterial secretion systems, as they can pass effector proteins directly into the cytoplasm of host cells. The correct prediction of type IV protein effectors secreted by T4SS is important, since they are known to play a noteworthy role in various human pathogens. Studies on predicting T4SS effectors involve traditional machine learning algorithms. In this work we included a deep learning architecture, i.e., a Convolutional Neural Network (CNN), to predict IVA and IVB effectors. Three feature extraction methods were utilized to represent each protein as an image and these images fed the CNN as inputs in our proposed framework. Pseudo proteins were generated using ADASYN algorithm to overcome the imbalanced dataset problem. We demonstrated that our framework predicted all IVA effectors correctly. In addition, the sensitivity performance of 94.2% for IVB effector prediction exhibited our framework’s ability to discern the effectors in unidentified proteins