Predicting existence of Mycobacterium tuberculosis on patients using data mining approaches

AbstractA correct diagnosis of tuberculosis (TB) can be only stated by applying a medical test to patient’s phlegm. The result of this test is obtained after a time period of about 45 days. The purpose of this study is to develop a data mining(DM) solution which makes diagnosis of tuberculosis as accurate as possible and helps deciding if it is reasonable to start tuberculosis treatment on suspected patients without waiting the exact medical test results or not.In this research, we proposed the use of Sugeno-type “adaptive-network-based fuzzy inference system” (ANFIS) to predict the existence of mycobacterium tuberculosis. 667 different patient records which are obtained from a clinic are used in the entire process of this research. Each of the patient records consist of 30 separate input parameters. ANFIS model is generated by using 500 of those records. We also implemented a multilayer perceptron and PART model using the same data set.The ANFIS model classifies the instances with an RMSE of 18% whereas Multilayer Perceptron does the same classification with an RMSE of % 19 and PART algorithm with an RMSE of % 20.ANFIS is an accurate and reliable method when compared with Multilayer Perceptron and PART algorithms for classification of tuberculosis patients. This study has contribution on forecasting patients before the medical tests

A survey on artificial intelligence based techniques for diagnosis of hepatitis variants

Hepatitis is a dreaded disease that has taken the lives of so many people over the recent past years. The research survey shows that hepatitis viral disease has five major variants referred to as Hepatitis A, B, C, D, and E. Scholars over the years have tried to find an alternative diagnostic means for hepatitis disease using artificial intelligence (AI) techniques in order to save lives. This study extensively reviewed 37 papers on AI based techniques for diagnosing core hepatitis viral disease. Results showed that Hepatitis B (30%) and C (3%) were the only types of hepatitis the AI-based techniques were used to diagnose and properly classified out of the five major types, while (67%) of the paper reviewed diagnosed hepatitis disease based on the different AI based approach but were not classified into any of the five major types. Results from the study also revealed that 18 out of the 37 papers reviewed used hybrid approach, while the remaining 19 used single AI based approach. This shows no significance in terms of technique usage in modeling intelligence into application. This study reveals furthermore a serious gap in knowledge in terms of single hepatitis type prediction or diagnosis in all the papers considered, and recommends that the future road map should be in the aspect of integrating the major hepatitis variants into a single predictive model using effective intelligent machine learning techniques in order to reduce cost of diagnosis and quick treatment of patients

A Clinical Prognostic Framework for Classifying Severe Liver Disorders (SLDs) and Lungs’ Vulnerability to Virus

Most severe liver diseases (SLDs) are attributed to increased risk for cancer, and cirrhosis, through which the manifestation of fibrotic tissues and scars tends to affect liver function The role of liver is indispensable, as inner organ performing services that ranges from metabolism, immune guide, energy producer and digestive aid, just to mention a few. Prevalence of classification problem and the need for automated prognosis is the continual drive to apply data mining techniques and/or machine learning algorithms in medical diagnosis and clinical support systems. Computational scientists and researchers in the field of artificial intelligence have recorded notable efforts with existing methods/models for diagnosis or prognosis, yet their effectiveness and functional performance is not without drawback due to ambiguity of medical information and selected features in patients’ data to tell the future course. In this paper, a novel hybridized machine learning model was provided (Fuzzy c-BC) for clinical classification of Severe Liver Disorders (SLDs) and to determine Lungs Vulnerability (LV) to virus; by incorporating individual strength of fuzzy cluster means (FCM) and naive Bayes classifier (NBC) for projecting future course of every categorized liver disease (LD) and its implication to aggravate lungs infection if preventive measures are not taken in timely manner

Development of Photovoltaic Power Generation Prediction Model using ANFIS

Prediction of photovoltaic (PV) performance is important for energy management practices. The power produced from renewable energy sources is uncertain in nature as it is subjected to continuous changing weather conditions. Hence accurate prediction of output power from these sources is difficult task. In this paper Adaptive Neuro-Fuzzy Inference System (ANFIS) based forecast model for predicting the PV power generation is developed. The proposed model is based on back propagation hybrid learning algorithm of ANFIS with four inputs and one output. Experimentally measured input data of 20 KW. PV system installed at Nashik, Maharashtra, India is used for developing prediction model. The inputs are solar radiation (Rad), ambient temperature (Temp), relative humidity (Hum) and day of year for measurement. Photovoltaic power generation is the output of the model. This data is utilized in the training and testing of the proposed model. Results obtained confirm the ability of the developed ANFIS model for assessing the power produced with reasonable accuracy. A comparative study has done between regression analysis and ANFIS. This shows that the ANFIS-model performs much better than regression. The advantage of the ANFIS model is that they do not need more parameters or complicate calculations unlike implicit models. The developed model could be used to forecast the profile of the produced power in uncertain whether conditions. The error due to ANFIS prediction model for energy produced from the given system considered in this research is 6.14 % which is much better when compared with regression analysis whose error is 16 %.  The results indicate that this model can potentially be used to estimate and predict PV solar output power

Fuzzy Logic in Decision Support: Methods, Applications and Future Trends

During the last decades, the art and science of fuzzy logic have witnessed significant developments and have found applications in many active areas, such as pattern recognition, classification, control systems, etc. A lot of research has demonstrated the ability of fuzzy logic in dealing with vague and uncertain linguistic information. For the purpose of representing human perception, fuzzy logic has been employed as an effective tool in intelligent decision making. Due to the emergence of various studies on fuzzy logic-based decision-making methods, it is necessary to make a comprehensive overview of published papers in this field and their applications. This paper covers a wide range of both theoretical and practical applications of fuzzy logic in decision making. It has been grouped into five parts: to explain the role of fuzzy logic in decision making, we first present some basic ideas underlying different types of fuzzy logic and the structure of the fuzzy logic system. Then, we make a review of evaluation methods, prediction methods, decision support algorithms, group decision-making methods based on fuzzy logic. Applications of these methods are further reviewed. Finally, some challenges and future trends are given from different perspectives. This paper illustrates that the combination of fuzzy logic and decision making method has an extensive research prospect. It can help researchers to identify the frontiers of fuzzy logic in the field of decision making

Classification of microarray gene expression cancer data by using artificial intelligence methods

Günümüzde bilgisayar teknolojilerinin gelişmesi ile birçok alanda yapılan çalışmaları etkilemiştir. Moleküler biyoloji ve bilgisayar teknolojilerinde meydana gelen gelişmeler biyoinformatik adlı bilimi ortaya çıkarmıştır. Biyoinformatik alanında meydana gelen hızlı gelişmeler, bu alanda çözülmeyi bekleyen birçok probleme çözüm olma yolunda büyük katkılar sağlamıştır. DNA mikroarray gen ekspresyonlarının sınıflandırılması da bu problemlerden birisidir. DNA mikroarray çalışmaları, biyoinformatik alanında kullanılan bir teknolojidir. DNA mikroarray veri analizi, kanser gibi genlerle alakalı hastalıkların teşhisinde çok etkin bir rol oynamaktadır. Hastalık türüne bağlı gen ifadeleri belirlenerek, herhangi bir bireyin hastalıklı gene sahip olup olmadığı büyük bir başarı oranı ile tespit edilebilir. Bireyin sağlıklı olup olmadığının tespiti için, mikroarray gen ekspresyonları üzerinde yüksek performanslı sınıflandırma tekniklerinin kullanılması büyük öneme sahiptir. DNA mikroarray’lerini sınıflandırmak için birçok yöntem bulunmaktadır. Destek Vektör Makinaları, Naive Bayes, k-En yakın Komşu, Karar Ağaçları gibi birçok istatistiksel yöntemler yaygın olarak kullanlmaktadır. Fakat bu yöntemler tek başına kullanıldığında, mikroarray verilerini sınıflandırmada her zaman yüksek başarı oranları vermemektedir. Bu yüzden mikroarray verilerini sınıflandırmada yüksek başarı oranları elde etmek için yapay zekâ tabanlı yöntemlerin de kullanılması yapılan çalışmalarda görülmektedir. Bu çalışmada, bu istatistiksel yöntemlere ek olarak yapay zekâ tabanlı ANFIS gibi bir yöntemi kullanarak daha yüksek başarı oranları elde etmek amaçlanmıştır. İstatistiksel sınıflandırma yöntemleri olarak K-En Yakın Komşuluk, Naive Bayes ve Destek Vektör Makineleri kullanılmıştır. Burada Göğüs ve Merkezi Sinir Sistemi kanseri olmak üzere iki farklı kanser veri seti üzerinde çalışmalar yapılmıştır. Sonuçlardan elde edilen bilgilere göre, genel olarak yapay zekâ tabanlı ANFIS tekniğinin, istatistiksel yöntemlere göre daha başarılı olduğu tespit edilmiştir

Diagnosis of Smear-Negative Pulmonary Tuberculosis using Ensemble Method: A Preliminary Research

Indonesia is one of 22 countries with the highest burden of Tuberculosis in the world. According to WHO’s 2015 report, Indonesia was estimated to have one million new tuberculosis (TB) cases per year. Unfortunately, only one-third of new TB cases are detected. Diagnosis of TB is difficult, especially in the case of smear-negative pulmonary tuberculosis (SNPT). The SNPT is diagnosed by TB trained doctors based on physical and laboratory examinations. This study is preliminary research that aims to determine the ensemble method with the highest level of accuracy in the diagnosis model of SNPT. This model is expected to be a reference in the development of the diagnosis of new pulmonary tuberculosis cases using input in the form of symptoms and physical examination in accordance with the guidelines for tuberculosis management in Indonesia. The proposed SNPT diagnosis model can be used as a cost-effective tool in conditions of limited resources. Data were obtained from medical records of tuberculosis patients from the Jakarta Respiratory Center. The results show that the Random Forest has the best accuracy, which is 90.59%, then Adaboost of 90.54% and Bagging of 86.91%

Determining customer limits by data mining methods in credit allocation process

The demand for credit is increasing constantly. Banks are looking for various methods of credit evaluation that provide the most accurate results in a shorter period in order to minimize their rising risks. This study focuses on various methods that enable the banks to increase their asset quality without market loss regarding the credit allocation process. These methods enable the automatic evaluation of loan applications in line with the sector practices, and enable determination of credit policies/strategies based on actual needs. Within the scope of this study, the relationship between the predetermined attributes and the credit limit outputs are analyzed by using a sample data set of consumer loans. Random forest (RF), sequential minimal optimization (SMO), PART, decision table (DT), J48, multilayer perceptron(MP), JRip, naïve Bayes (NB), one rule (OneR) and zero rule (ZeroR) algorithms were used in this process. As a result of this analysis, SMO, PART and random forest algorithms are the top three approaches for determining customer credit limits