Eamining and Comparing Data Mining-Based Techniques for Hepatitis Diagnosis

ABSTRACT: Increasing advances in information technology has led to significant growth in sciences. One of the fields in which significant changes has occurred is the medical field. Using data-mining techniques in this branch of science has helped physicians in all subjects, in particular diagnosis of sicknesses. Hepatitis diagnosis is highly difficult due to limited clinical diagnosis of the disease in its early stages. To this end, this paper tries to introduce and recommend the best way to diagnose hepatitis as well as to compare common clustering methods such as decision trees, neural networks, and SVM. Evaluation criteria of classification methods are the accuracy of each of methods and Clementine software along with data base in the University of California has been used to test each method. Obtained results show that neural network algorithm enjoys higher accuracy in comparison with other algorithms. Using neural network algorithm can accurately predict 89.74% hepatitis

DECISSION SUPPORT SYSTEM DI AMERIKA: SEBUAH EKISTENSI GLOBAL DAN MASADEPAN

The Industrial Revolution in America was influenced by technological development to become a highly developed country. Information technology combined with the Decision Support System (DSS) is able to give a new face to the application of technology in several fields, including: 1). Industry; 2). Health; 3). Agriculture; 4). Living environment ; 5). Military; and 6). Education. DSS was known in 1970 due to limitations in the development of technology for the military field. However, gradually the development of DSS can be applied in several fields. In this study using the Systematic Literature Review method with 4 stages of research, including: a). Research Questions; b). Inclusion Criterl; c). Identification of Papers; and D). Conclusions are carried out by grouping according to categories in certain years, including: 1). 1980-1990; 2). 1990-2000; 3). 2000-2010; and 4). 2010-2020. This research outlines an analysis of how the development of the use of DSS in various fields from year to year in America can be used as a policy maker and evaluating the use of technological trends in several fields. The results of this study are. The conclusion of this research is the field of industry and the health field is more dominant in use in the decision support system. Because the decision support system is very needed in the field. And as the time goes by, the use of the decision support system is increasingly reduced, even in the year 2010 to 2020, the use of a decision support system is called as artificial intelligence. Because artificial intelligence technology includes a decision support system in it. And artificial intelligence has a broader meaning is not merely a decision support system.Revolusi Industri di Amerika dipengaruhi oleh pengembangan teknologi hingga menjadi negara yang sangat maju. Teknologi informasi yang dipadukan dengan Decision Support System (DSS) mampu memberikan wajah baru pada penerapan teknologi dibeberapa bidang, antara lain: 1). Industri; 2). Kesehatan; 3). Pertanian;  4). Lingkungan Hidup ; 5). Militer; dan 6). Pendidikan. DSS dikenal pada tahun 1970 dengan keterbatasan pada pengembangan teknologi untuk bidang militer. Namun, secara bertahap pengembangan DSS dapat diaplikasikan pada beberapa bidang. Pada penelitian ini menggunakan metode Systematic  Literature  Review dengan 4 tahapan penelitian, antara lain: a). Research Questions; b). Inclusion Criterl;  c). Identification of Papers; dan  d). Conclusion yang dilakukan dengan mengelompokkan sesuai kategori pada beberapa tahun tertentu, antara lain: 1). 1980-1990; 2). 1990-2000; 3). 2000-2010; dan 4). 2010-2020 . Penelitian ini secara garis besar melakukan analisis bagaimana perkembangan penggunaan DSS pada berbagai bidang dari tahun ke tahun di Amerika yang dapat digunakan sebagai pengambil kebijakan serta evaluasi penggunaan tren teknologi dalam beberapa bidang. Kesimpulan dari penelitian ini adalah bidang industri dan bidang kesehatan lebih dominan di gunakan pada Decision Support System. Karena Decision Support System sangat di butuhkan dalam bidang tersebut. Dan seiring perkembangan zaman, penggunaan Decision Support System semakin berkurang, bahkan pada tahun 2010 menuju tahun 2020, penggunaan Decision Support System di sebut sebagai kecerdasan buatan. Karena teknologi kecerdasan buatan telah mencakup Decision Support System di dalamnya. Dan kecerdasan buatan memiliki arti yang lebih luas bukan hanya sekedar Decision Support System

A predictive method for hepatitis disease diagnosis using ensembles of neuro-fuzzy technique

Background: Hepatitis is an inflammation of the liver, most commonly caused by a viral infection. Supervised data mining techniques have been successful in hepatitis disease diagnosis through a set of datasets. Many methods have been developed by the aids of data mining techniques for hepatitis disease diagnosis. The majority of these methods are developed by single learning techniques. In addition, these methods do not support the ensemble learning of the data. Combining the outputs of several predictors can result in improved accuracy in classification problems. This study aims to propose an accurate method for the hepatitis disease diagnosis by taking the advantages of ensemble learning. Methods: We use Non-linear Iterative Partial Least Squares to perform the data dimensionality reduction, Self-Organizing Map technique for clustering task and ensembles of Neuro-Fuzzy Inference System for predicting the hepatitis disease. We also use decision trees for the selection of most important features in the experimental dataset. We test our method on a real-world dataset and present our results in comparison with the latest results of previous studies. Results: The results of our analyses on the dataset demonstrated that our method performance is superior to the Neural Network, ANFIS, K-Nearest Neighbors and Support Vector Machine. Conclusions: The method has potential to be used as an intelligent learning system for hepatitis disease diagnosis in the healthcare. © 2018 The Author