Application of Data Mining In Predicting Herpes Disease Using the C4.5 Algorithm (original) (raw)
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Predicting Common Diseases Among Students using Decision Tree (J48) Classification Algorithm
International journal of academic research in business & social sciences, 2021
Predictive analysis is very useful in the process of decision making. It discovers useful information by predicting the future outcome. Nevertheless, it is essential to understand an appropriate technique before the predictive analytical model should be developed. This research will compare two predictive methods which are decision tree technique (using J48 algorithm) and rule induction technique (using JRip algorithm). The aim of this research is to build the predictive model for health datasets of students in one of the universities in Selangor. By analyzing medical profiles such as gender, diseases, the symptoms of the diseases, the organs of the diseases and the body systems of diseases, the model can predict the likelihood of disease that may occur in the future. It can offer significant and important insights, such as the patterns and relationships between medical attributes related to the diagnosis datasets. In this study, we are also able to identify the most common illness that may have infected the students in the past five years. This data analysis could be beneficial specifically to the health center to plan, coordinate tasks and make better decisions.
An Analysis of Hepatitis C Virus Prediction Using Different Data Mining Techniques
TJPRC, 2013
The prediction of hepatitis C virus (HCV) is a significant and tedious task in medicine. The healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’. There is a wealth of data available within the healthcare system. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. Using medical profile such as age, sex, residence and (ALT, AST) enzyme blood tests it can predict the likelihood of patients getting HCV infection. It enables significant knowledge, e.g. patterns, relationships between medical factors related to HCV, to be established. It can serve a training tool to train nurses and medical students to diagnose patients infected with HCV. This paper analyses the performance of various classification function techniques in data mining for predicting the infection with HCV from the HCV data set. These Techniques are Decision Trees, Naïve Bayes and Neural Network. Results show that each technique has its unique strength in realizing the objectives of the defined mining goals. Also the performance of three data mining techniques is compared using three data sets of different size.
A Decision Tree Based Classifier for Classification & Prediction of Diseases
IJSRD, 2013
In this paper, we are proposing a modified algorithm for classification. This algorithm is based on the concept of the decision trees. The proposed algorithm is better then the previous algorithms. It provides more accurate results. We have tested the proposed method on the example of patient data set. Our proposed methodology uses greedy approach to select the best attribute. To do so the information gain is used. The attribute with highest information gain is selected. If information gain is not good then again divide attributes values into groups. These steps are done until we get good classification/misclassification ratio. The proposed algorithms classify the data sets more accurately and efficiently.
Proceedings of the 8th International Conference on Pervasive Computing Technologies for Healthcare, 2014
In data mining, classification is one of the significant techniques with applications in fraud detection, Artificial intelligence, Medical Diagnosis and many other fields. Classification of objects based on their features into predefined categories is a widely studied problem. Decision trees are very much useful to diagnose a patient problem by the physicians. Decision tree classifiers are used extensively for diagnosis of breast tumour in ultrasonic images, ovarian cancer and heart sound diagnosis. In this paper, performance of decision tree induction classifiers on various medical data sets in terms of accuracy and time complexity are analysed.
2021
Svitlana Surodina, BSc, MBA; Ching Lam, MEng; Svetislav Grbich, MSc; Madison Milne-Ives, BAS, MSc; Michelle van Velthoven, BSc, MSc, PhD; Edward Meinert, MA, MSc, MBA, MPA, PhD, CEng FBCS EUR ING 1Skein Ltd, London, United Kingdom 2Department of Informatics, King’s College London, London, United Kingdom 3Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom 4Centre for Health Technology, University of Plymouth, Plymouth, United Kingdom 5Nuffield Department of Primary Health Sciences, Medical Sciences Division, University of Oxford, Oxford, United Kingdom 6Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
Decision Tree based Health Prediction System
International Journal for Research in Applied Science and Engineering Technology IJRASET, 2020
Now a days, diseases have become one of the major causes of human death. To reduce this rising growth of medical problems, data mining techniques have been popularized on higher scale. It has potentially enhanced the clinical decisions and survival time of patients. But choosing appropriate data mining technique is the main task because accuracy is the main issue. The paper presents an overview of the decision tree technique with its medical aspects of Disease Prediction. Major objective is to evaluate decision tree technique in clinical and health care applications to develop accurate decisions. It uses already existing data in different databases to transform it into new research and accurate results. Managing patient's historical data is also made easy and less complex with less effort. This paper describes health prediction results online using decision tree technique with maximum accuracy and presents a glimpse of how this website will work.
2019
Most disease surveillance outfits and authrorities around the world battle with one key challenge – the useful and objective handling and processing of the huge sets of disease data being generated on a regular basis as their personnel exercise their disease surveillance mandate. Many theories have been put forth on how best this could be tackled. Among these, is the use of information technology and mathematical theories and concepts to alleviate the problem. One of the most solid and promising methods includes the use of artificial intelligence techniques to help break down and make good sense of the data sets. This research looks to compare the usage of the C4.5 and the ID3 decision tree theory concepts as means of tackling making the best of disease surveillance data. The C4.5 and ID3 algorithms provide a method of breaking down the data and generating (among other useful information) the entropies and information gains of some predefined variables from huge sets of disease outb...
A COMPUTATIONAL INTELLIGENCE TECHNIQUE FOR EFFECTIVE MEDICAL DIAGNOSIS USING DECISION TREE ALGORITHM
Now-a-day's humankind suffering with many health complications. This century's the people affected by most progressive diseases (like as Heart disease, Diabetes disease, AIDS disease, Hepatitis disease and Fibroid diseases) and its complications. Data mining (also called as knowledge discovery) is the process of summarizing the data into useful information by analyzing data from different perspectives. Data Mining is a technology for processing large volume of data that combines traditional data analysis methods with highly developed algorithms. Data mining techniques can be used to support a wide range of security and business applications such as work flow management, customer profiling and fraud detection. It can be also used to predict the outcome of future observations. The Data mining techniques can be developed by the Decision tree algorithm. According to recent survey of World Health Organization (WHO), all diseases and its complications are problematical health hazards of this century. A better and early diagnosis of disease may improve the lives of all people affected and people may lead healthy life. In this paper, the authors present the Decision tree algorithm for better diagnosis of Diseases using association rule mining. In this computational intelligence techniques the authors tested the performance of the method using disease data sets. The authors presented a better algorithm which is used to calculate sensitivity, specificity comprehensibility and rule length. This gain and gain ratio achieved for promising accuracy.
A Review of A Novel Decision Tree Based Classifier for Accurate Multi Disease Prediction
International Journal For Scientific Research and Development, 2015
Many researchers have worked on the disease prediction systems using the data mining techniques. Some of the systems are for predicting a single disease and some for the predicting the multiple diseases. Still there is scope to improve the efficiency of the disease prediction. In this synopsis, we are presenting a novel classification based disease prediction system. It uses the concept of classification using the decision tree. Our proposed technique uses greedy approach to select the best attribute for construction of the decision tree. The modified information gain is used. The attribute with highest information gain is selected as the root of the tree. The experimental results have shown that the proposed algorithms classify the data sets more accurately and efficiently. In this synopsis, we have also presented an overview of existing data classification algorithms. These algorithms are described more or less on their own. Data Classification is a very popular and computationally expensive task. We have also elaborated the fundamentals of data classification. From a large number of available algorithms that have been developed we will compare the most important ones.
Classification of the Risk Types of Human Papillomavirus by Decision Trees
Lecture Notes in Computer Science, 2003
The high-risk type of Human Papillomavirus (HPV) is the main etiologic factor of cervical cancer, which is a leading cause of cancer deaths in women worldwide. Therefore, classifying the risk type of HPVs is very useful and necessary to the daignosis and remedy of cervical cancer. In this paper, we classify the risk type of 72 HPVs and predict the risk type of 4 HPVs of which type is unknown. As a machine learning method to classify them, we use decision trees. According to the experimental results, it shows about 81.14% of accuracy.