25-08-2016, 09:34 AM
[attachment=69817]
Abstract— Decision making is becoming more complex and stressful for individuals and groups especially for medical professionals as a result of huge quantity of data. This paper is on the use of decision tree for treatment options in medical decision support systems. Decision tree was developed and an algorithm to identify optimal choice among complicated options in Surgery (Medical Operation) and Medical management (Drug Prescription) by calculating probabilities of events and incorporating patient evaluations of possible outcomes based on Average Life Year (ALY). This can help the medical professionals in taking decision among the available choices.
Keywords— Machine learning, Decision Tree, Decision making, Surgery, Medical Management, Average Life Year.
I. INTRODUCTION
As a result of huge quantity of data, decision making is becoming more complex for individuals and group that are making them. This is where the need for a good decision support technique arises. The decision support system should be able to process those huge data and to help the medical professionals in making their decisions stress-free and more reliably so as to eliminate mismanagement and erroneous diagnosis of patient [11].
Machine Learning (ML) is a branch of Computer Science that is concerned with designing systems that can learn from the provided inputs. Usually the systems are designed to use this learned knowledge to better process of similar inputs in the future.
ML is an area of artificial intelligence that uses algorithms to improve performance over time and to automatically learn programs from data [5]. ML is being used for the analysis of the clinical factor significance and their combinations for the prediction of progression in disease, extraction for outcomes research in medical knowledge, therapy planning and support and general management of patient [14].
The use of ML methods can offer useful aids to assist the physician in many cases, eliminate issues related to human fatigue, provide rapid identification of abnormalities and allow diagnosis in real time. ML is also being used for data analysis, such as clarification of constant data used in the Intensive Care Unit, exposure of consistencies in the data by properly dealing with imperfect data, and for intelligent alarm resulting in effective and efficient monitoring. It is argued that the successful implementation of ML methods can help the integration of computer-based systems in the healthcare environment providing opportunities to facilitate and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care. In the last decade the use of machine learning has increased rapidly throughout computer science and beyond. ML is used in spam filters, Web search, fraud detection, credit scoring, stock trading, drug design, recommender systems and other applications.
Machine learning methods can be classified as supervised and unsupervised [4]. Supervised methods are trained with labeled data; that is, cases that have known outcomes. Unsupervised methods learn from unlabeled data, and data are grouped based on similarity. Medical errors are both expensive and risky [9]. The death caused by medical errors in U.S hospital each year has increased drastically more than even from cancer, Aids and road accidents combined [19]. The result of the research carried out by the National Patient Safety Foundation founds that about 42 percent of over 100 million Americans believed that they had personally experienced a medical mistake [15].
This paper is organized as follows. Section 2 discusses Decision tree, Section 3 discusses machine learning and its problems. Section 4 discusses related works. Section 5 presents the method used, describes and discusses the validity of the obtained results. And finally, Section 6 concludes the research results.
II. DECISION TREE
Machine learning (ML) to medical decision making can be describe as tools for solving diagnostic problems in medical fields. Decision tree is one of the most common, consistent, effective, powerful and popular classification and prediction techniques that is used in machine learning process and decision making analysis. It is a clear, measurable, and efficient method to decision making under conditions of uncertainty with a simple illustration of gathered information.
A decision tree is a method that can help in making good choices, especially decisions that involve high costs and risks. Decision trees use a graphic approach to compare competing alternatives and assign values to those alternatives by combining uncertainties, costs, and payoffs into specific numerical values [17]. The automatic learning of decision trees
© 2015, IJARCSSE All Rights Reserved Page | 71
Mamman et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(2), February - 2015, pp. 71-78 and their use usually show very good results in various “theoretical” environments. Inability to measure quality values,
high cost and complexity of such measurements and unavailability of all attributes at the same time are the typical representatives. In medical decision making, there are many conditions where decision must be made efficiently and unfailingly. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision tree is one of the most common, consistent, effective, powerful and popular classification and prediction techniques that is used in machine learning process and decision making analysis. It provides high classification precision with a simple illustration of gathered information [17].
III. MACHINE LEARNING AND ITS PROBLEMS
Machine Learning is the study of techniques for programming computers to learn. Computer applications are wide to range of tasks, and for most of these it is quite easy for programmers to design and implement the necessary software. Machine learning is concern primarily with the accuracy and effectiveness of the resulting computer system. Though, there are many tasks for which this is difficult or impossible [18]. Prediction rules can be learnt from the data recorded by the machine learning system and consequent failure arising from the machine [3]. Secondly, where the human experts are available but are unable to explain their expertise, this is also a problem that makes machine learning difficult or impossible. In tasks such as natural language understanding, hand writing identification and speech recognition, no human being can tell or give details of steps they follow in performing them because of their expert level abilities on these tasks. Coincidentally, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs [18].
Thirdly, there are problems where events changes promptly. Taking finance for instance where the future behavior of the stock market would be predicted and the consumer purchases alongside with the exchange rate. These behaviors often change and if a programmer tries to build a good predictive computer program, such program would constantly be rewritten and frequently modified the learned prediction rule which will definitely become a burden [3].
Finally, since it is not feasible to provide each computer user with a software engineer in order to keep the rule updated, it is not also reasonable to program each user computer to his/her own rules. Considering a program to filter unwanted mail messages, this will be impossible because different users will need different filters. This is to say that there will be customization of each computer users independently which also constitute a problem to machine learning
Abstract— Decision making is becoming more complex and stressful for individuals and groups especially for medical professionals as a result of huge quantity of data. This paper is on the use of decision tree for treatment options in medical decision support systems. Decision tree was developed and an algorithm to identify optimal choice among complicated options in Surgery (Medical Operation) and Medical management (Drug Prescription) by calculating probabilities of events and incorporating patient evaluations of possible outcomes based on Average Life Year (ALY). This can help the medical professionals in taking decision among the available choices.
Keywords— Machine learning, Decision Tree, Decision making, Surgery, Medical Management, Average Life Year.
I. INTRODUCTION
As a result of huge quantity of data, decision making is becoming more complex for individuals and group that are making them. This is where the need for a good decision support technique arises. The decision support system should be able to process those huge data and to help the medical professionals in making their decisions stress-free and more reliably so as to eliminate mismanagement and erroneous diagnosis of patient [11].
Machine Learning (ML) is a branch of Computer Science that is concerned with designing systems that can learn from the provided inputs. Usually the systems are designed to use this learned knowledge to better process of similar inputs in the future.
ML is an area of artificial intelligence that uses algorithms to improve performance over time and to automatically learn programs from data [5]. ML is being used for the analysis of the clinical factor significance and their combinations for the prediction of progression in disease, extraction for outcomes research in medical knowledge, therapy planning and support and general management of patient [14].
The use of ML methods can offer useful aids to assist the physician in many cases, eliminate issues related to human fatigue, provide rapid identification of abnormalities and allow diagnosis in real time. ML is also being used for data analysis, such as clarification of constant data used in the Intensive Care Unit, exposure of consistencies in the data by properly dealing with imperfect data, and for intelligent alarm resulting in effective and efficient monitoring. It is argued that the successful implementation of ML methods can help the integration of computer-based systems in the healthcare environment providing opportunities to facilitate and enhance the work of medical experts and ultimately to improve the efficiency and quality of medical care. In the last decade the use of machine learning has increased rapidly throughout computer science and beyond. ML is used in spam filters, Web search, fraud detection, credit scoring, stock trading, drug design, recommender systems and other applications.
Machine learning methods can be classified as supervised and unsupervised [4]. Supervised methods are trained with labeled data; that is, cases that have known outcomes. Unsupervised methods learn from unlabeled data, and data are grouped based on similarity. Medical errors are both expensive and risky [9]. The death caused by medical errors in U.S hospital each year has increased drastically more than even from cancer, Aids and road accidents combined [19]. The result of the research carried out by the National Patient Safety Foundation founds that about 42 percent of over 100 million Americans believed that they had personally experienced a medical mistake [15].
This paper is organized as follows. Section 2 discusses Decision tree, Section 3 discusses machine learning and its problems. Section 4 discusses related works. Section 5 presents the method used, describes and discusses the validity of the obtained results. And finally, Section 6 concludes the research results.
II. DECISION TREE
Machine learning (ML) to medical decision making can be describe as tools for solving diagnostic problems in medical fields. Decision tree is one of the most common, consistent, effective, powerful and popular classification and prediction techniques that is used in machine learning process and decision making analysis. It is a clear, measurable, and efficient method to decision making under conditions of uncertainty with a simple illustration of gathered information.
A decision tree is a method that can help in making good choices, especially decisions that involve high costs and risks. Decision trees use a graphic approach to compare competing alternatives and assign values to those alternatives by combining uncertainties, costs, and payoffs into specific numerical values [17]. The automatic learning of decision trees
© 2015, IJARCSSE All Rights Reserved Page | 71
Mamman et al., International Journal of Advanced Research in Computer Science and Software Engineering 5(2), February - 2015, pp. 71-78 and their use usually show very good results in various “theoretical” environments. Inability to measure quality values,
high cost and complexity of such measurements and unavailability of all attributes at the same time are the typical representatives. In medical decision making, there are many conditions where decision must be made efficiently and unfailingly. Conceptual simple decision making models with the possibility of automatic learning are the most appropriate for performing such tasks. Decision tree is one of the most common, consistent, effective, powerful and popular classification and prediction techniques that is used in machine learning process and decision making analysis. It provides high classification precision with a simple illustration of gathered information [17].
III. MACHINE LEARNING AND ITS PROBLEMS
Machine Learning is the study of techniques for programming computers to learn. Computer applications are wide to range of tasks, and for most of these it is quite easy for programmers to design and implement the necessary software. Machine learning is concern primarily with the accuracy and effectiveness of the resulting computer system. Though, there are many tasks for which this is difficult or impossible [18]. Prediction rules can be learnt from the data recorded by the machine learning system and consequent failure arising from the machine [3]. Secondly, where the human experts are available but are unable to explain their expertise, this is also a problem that makes machine learning difficult or impossible. In tasks such as natural language understanding, hand writing identification and speech recognition, no human being can tell or give details of steps they follow in performing them because of their expert level abilities on these tasks. Coincidentally, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs [18].
Thirdly, there are problems where events changes promptly. Taking finance for instance where the future behavior of the stock market would be predicted and the consumer purchases alongside with the exchange rate. These behaviors often change and if a programmer tries to build a good predictive computer program, such program would constantly be rewritten and frequently modified the learned prediction rule which will definitely become a burden [3].
Finally, since it is not feasible to provide each computer user with a software engineer in order to keep the rule updated, it is not also reasonable to program each user computer to his/her own rules. Considering a program to filter unwanted mail messages, this will be impossible because different users will need different filters. This is to say that there will be customization of each computer users independently which also constitute a problem to machine learning