The document describes a proposed clinical decision support system that uses k-means clustering and an artificial neural network with particle swarm optimization to classify patient data and determine diagnoses. It begins with background on clinical decision making and existing systems. It then outlines the proposed system, which involves clustering patient data using k-means, and training an artificial neural network using particle swarm optimization and backpropagation to classify new patient data and determine optimal treatment. The combination of these techniques is meant to improve accuracy, efficiency, time consumption and costs compared to other methods.
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical
research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more
number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well
as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
Psdot 14 using data mining techniques in heartZTech Proje
FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS Z Technologies, Chennai
A Survey on Heart Disease Prediction Techniquesijtsrd
Heart disease is the main reason for a huge number of deaths in the world over the last few decades and has evolved as the most life threatening disease. The health care industry is found to be rich in information. So, there is a need to discover hidden patterns and trends in them. For this purpose, data mining techniques can be applied to extract the knowledge from the large sets of data. Many researchers, in recent times have been using several machine learning techniques for predicting the heart related diseases as it can predict the disease effectively. Even though a machine learning technique proves to be effective in assisting the decision makers, still there is a scope for developing an accurate and efficient system to diagnose and predict the heart diseases thereby helping doctors with ease of work. This paper presents a survey of various techniques used for predicting heart disease and reviews their performance. G. Niranjana | Dr I. Elizabeth Shanthi "A Survey on Heart Disease Prediction Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38349.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38349/a-survey-on-heart-disease-prediction-techniques/g-niranjana
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
AN ALGORITHM FOR PREDICTIVE DATA MINING APPROACH IN MEDICAL DIAGNOSISijcsit
The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical
research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more
number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well
as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
Psdot 14 using data mining techniques in heartZTech Proje
FINAL YEAR IEEE PROJECTS,
EMBEDDED SYSTEMS PROJECTS,
ENGINEERING PROJECTS,
MCA PROJECTS,
ROBOTICS PROJECTS,
ARM PIC BASED PROJECTS, MICRO CONTROLLER PROJECTS Z Technologies, Chennai
A Survey on Heart Disease Prediction Techniquesijtsrd
Heart disease is the main reason for a huge number of deaths in the world over the last few decades and has evolved as the most life threatening disease. The health care industry is found to be rich in information. So, there is a need to discover hidden patterns and trends in them. For this purpose, data mining techniques can be applied to extract the knowledge from the large sets of data. Many researchers, in recent times have been using several machine learning techniques for predicting the heart related diseases as it can predict the disease effectively. Even though a machine learning technique proves to be effective in assisting the decision makers, still there is a scope for developing an accurate and efficient system to diagnose and predict the heart diseases thereby helping doctors with ease of work. This paper presents a survey of various techniques used for predicting heart disease and reviews their performance. G. Niranjana | Dr I. Elizabeth Shanthi "A Survey on Heart Disease Prediction Techniques" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-2 , February 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38349.pdf Paper Url: https://www.ijtsrd.com/computer-science/other/38349/a-survey-on-heart-disease-prediction-techniques/g-niranjana
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
As we know that health care industry is completely based on assumptions, which after get tested and verified via various tests and patient have to be depend on the doctors knowledge on that topic . so we made a system that uses data mining techniques to predict the health of a person based on various medical test results. so we can predict the health of that person based on that analysis performed by the system.The system currently design only for heart issues, for that we had used Statlog (Heart) Data Set from UCI Machine Learning Repository it includes attributes like age, sex, chest pain type, cholesterol, sugar, outcomes,etc.for training the system. we only need to passed few general inputs in order to generate the prediction and the prediction results from all algorithms are they merged together by calculating there mean value that value shows the actual outcome of the prediction process which entirely works in background
Prognosis of Cardiac Disease using Data Mining Techniques A Comprehensive Surveyijtsrd
The Healthcare exchange generally clinical diagnosis is ended commonly by doctor's knowledge and practice. Computer Aided Decision Support System plays a major task in the medical field. Data mining provides the methodology and technology to modify these rises of data into valuable data for decision making. By utilizing data mining techniques it requires less time for the prediction of the diseases with more accuracy. Among the expanding research on coronary diseases predicting system, it has happened significant to classifications the exploration results and gives readers with a layout of the current coronary diseases forecast strategies in every discussion. Data mining tools can respond to exchange addresses that expectedly being used much time over riding to decide. In this paper we study different papers in which at least one algorithm of data mining used for the prediction of coronary diseases. As of the study it is observed that Naïve Bayes Technique increase the accuracy of the coronary diseases prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are outlined in this paper. D. Haripriya | Dr. M. Lovelin Ponn Felciah "Prognosis of Cardiac Disease using Data Mining Techniques: A Comprehensive Survey" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26605.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26605/prognosis-of-cardiac-disease-using-data-mining-techniques-a-comprehensive-survey/d-haripriya
Heart Disease Prediction Using Data Mining TechniquesIJRES Journal
There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease to be established. Performance of these techniques is compared through sensitivity, specificity and accuracy. It has been observed that Artificial Neural Networks outperform K Means clustering in all the parameters i.e. Sensitivity, Specificity and Accuracy.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
This describes the techniques that are used for prediction of heart diseases using the concept of data mining.It states about IHPDS(Intelligent Heart Disease Prediction System)
A comparative analysis of classification techniques on medical data setseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
Prognosis of Cardiac Disease using Data Mining Techniques A Comprehensive Surveyijtsrd
The Healthcare exchange generally clinical diagnosis is ended commonly by doctor's knowledge and practice. Computer Aided Decision Support System plays a major task in the medical field. Data mining provides the methodology and technology to modify these rises of data into valuable data for decision making. By utilizing data mining techniques it requires less time for the prediction of the diseases with more accuracy. Among the expanding research on coronary diseases predicting system, it has happened significant to classifications the exploration results and gives readers with a layout of the current coronary diseases forecast strategies in every discussion. Data mining tools can respond to exchange addresses that expectedly being used much time over riding to decide. In this paper we study different papers in which at least one algorithm of data mining used for the prediction of coronary diseases. As of the study it is observed that Naïve Bayes Technique increase the accuracy of the coronary diseases prediction system. The commonly used techniques for Heart Disease Prediction and their complexities are outlined in this paper. D. Haripriya | Dr. M. Lovelin Ponn Felciah "Prognosis of Cardiac Disease using Data Mining Techniques: A Comprehensive Survey" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26605.pdfPaper URL: https://www.ijtsrd.com/computer-science/data-miining/26605/prognosis-of-cardiac-disease-using-data-mining-techniques-a-comprehensive-survey/d-haripriya
Heart Disease Prediction Using Data Mining TechniquesIJRES Journal
There are huge amounts of data in the medical industry which is not processed properly and hence cannot be used effectively in making decisions. We can use data mining techniques to mine these patterns and relationships. This research has developed a prototype Heart Disease Prediction using data mining techniques, namely Neural Network, K-Means Clustering and Frequent Item Set Generation. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease to be established. Performance of these techniques is compared through sensitivity, specificity and accuracy. It has been observed that Artificial Neural Networks outperform K Means clustering in all the parameters i.e. Sensitivity, Specificity and Accuracy.
HEALTH PREDICTION ANALYSIS USING DATA MININGAshish Salve
Data mining techniques are used for a variety of applications. In healthcare industry, datamining plays an important
role in predicting diseases. For detecting a disease number of tests should be required from the patient. But using data
mining technique the number of tests can be reduced. This reduced test plays an important role in time and performance.
This report analyses data mining techniques which can be used for predicting different types of diseases. This report reviewed
the research papers which mainly concentrate on predicting various disease
A major challenge facing healthcare organizations (hospitals, medical centers) is
the provision of quality services at affordable costs. Quality service implies diagnosing
patients correctly and administering treatments that are effective. Poor clinical decisions
can lead to disastrous consequences which are therefore unacceptable. Hospitals must
also minimize the cost of clinical tests. They can achieve these results by employing
appropriate computer-based information and/or decision support systems.
Most hospitals today employ some sort of hospital information systems to manage
their healthcare or patient data.
These systems are designed to support patient billing, inventory management and generation of simple statistics. Some hospitals use decision support systems, but they are largely limited. Clinical decisions are often made based on doctors’ intuition and experience rather than on the knowledge rich data hidden in the database.
This practice leads to unwanted biases, errors and excessive medical costs which affects the quality of service provided to patients.
Smart Health Prediction Using Data Mining.Data mining is a new powerful technology which is of high interest in computer world. It is a sub field of computer science that uses already existing data in different databases to transform it into new researches and results. It makes use of Artificial Intelligence, machine learning and database management to extract new patterns from large data sets and the knowledge associated with these patterns. The actual task is to extract data by automatic or semi-automatic means. The different parameters included in data mining includes clustering, forecasting, path analysis and predictive analysis.
This describes the techniques that are used for prediction of heart diseases using the concept of data mining.It states about IHPDS(Intelligent Heart Disease Prediction System)
A comparative analysis of classification techniques on medical data setseSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology.
The Healthcare industry contains big and complex data that may be required in order to discover fascinating pattern of diseases & makes effective decisions with the help of different machine learning techniques. Advanced data mining techniques are used to discover knowledge in database and for medical research. This paper has analyzed prediction systems for Diabetes, Kidney and Liver disease using more number of input attributes. The data mining classification techniques, namely Support Vector Machine(SVM) and Random Forest (RF) are analyzed on Diabetes, Kidney and Liver disease database. The performance of these techniques is compared, based on precision, recall, accuracy, f_measure as well as time. As a result of study the proposed algorithm is designed using SVM and RF algorithm and the experimental result shows the accuracy of 99.35%, 99.37 and 99.14 on diabetes, kidney and liver disease respectively.
ARTIFICIAL NEURAL NETWORKING.
FIRST STEP TO KNOWLEDGE IS TO KNOW THAT we are ignorant
Knowledge in medical field is characterized by uncertanity and vagueness
Historically as well as currently this fact remains a motivation for the development of medical decision support system are based on fuzzy logics
Greek philosopher visualized a basic model of brain function as early as 300 bc
Till date nervous system is not completely understood to human kind.
In order to cope with real-world problems more effectively, we tend to design a decision support system for tuberculosis bacterium class identification. In this paper, we are concerned to propose a fuzzy diagnosability approach, which takes value between {0, 1} and based on observability of events, we
formalized the construction of diagnoses that are used to perform diagnosis. In particular, we present a
framework of the fuzzy expert system; discuss the suitability of artificial intelligence as a novel soft paradigm and reviews work from the literature for the development of a medical diagnostic system. The newly proposed approach allows us to deal with problems of diagnosability for both crisp and fuzzy value of input data. Accuracy analysis of designed decision support system based on demographic data was done
by comparing expert knowledge and system generated response. This basic emblematic approach using
fuzzy inference system is presented that describes a technique to forecast the existence of bacterium and
provides support platform to pulmonary researchers in identifying the ailment effectively.
A Decision Support System for Tuberculosis Diagnosability ijsc
In order to cope with real-world problems more effectively, we tend to design a decision support system for tuberculosis bacterium class identification. In this paper, we are concerned to propose a fuzzy diagnosability approach, which takes value between {0, 1} and based on observability of events, we formalized the construction of diagnoses that are used to perform diagnosis. In particular, we present a framework of the fuzzy expert system; discuss the suitability of artificial intelligence as a novel soft paradigm and reviews work from the literature for the development of a medical diagnostic system. The newly proposed approach allows us to deal with problems of diagnosability for both crisp and fuzzy value of input data. Accuracy analysis of designed decision support system based on demographic data was done by comparing expert knowledge and system generated response. This basic emblematic approach using fuzzy inference system is presented that describes a technique to forecast the existence of bacterium and provides support platform to pulmonary researchers in identifying the ailment effectively.
prediction of heart disease using machine learning algorithmsINFOGAIN PUBLICATION
The successful experiment of data mining in highly visible fields like marketing, e-business, and retail has led to its application in other sectors and industries. Healthcare is being discovered among these areas. There is an opulence of data available within the healthcare systems. However, there is a scarcity of useful analysis tool to find hidden relationships in data. This research intends to provide a detailed description of Naïve Bayes and decision tree classifier that are applied in our research particularly in the prediction of Heart Disease. Some experiment has been conducted to compare the execution of predictive data mining technique on the same dataset, and the consequence reveals that Decision Tree outperforms over Bayesian classification.
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
An internet of things-based automatic brain tumor detection systemIJEECSIAES
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
An internet of things-based automatic brain tumor detection systemnooriasukmaningtyas
Due to the advances in information and communication technologies, the usage of the internet of things (IoT) has reached an evolutionary process in the development of the modern health care environment. In the recent human health care analysis system, the amount of brain tumor patients has increased severely and placed in the 10th position of the leading cause of death. Previous state-of-the-art techniques based on magnetic resonance imaging (MRI) faces challenges in brain tumor detection as it requires accurate image segmentation. A wide variety of algorithms were developed earlier to classify MRI images which are computationally very complex and expensive. In this paper, a cost-effective stochastic method for the automatic detection of brain tumors using the IoT is proposed. The proposed system uses the physical activities of the brain to detect brain tumors. To track the daily brain activities, a portable wrist band named Mi Band 2, temperature, and blood pressure monitoring sensors embedded with Arduino-Uno are used and the system achieved an accuracy of 99.3%. Experimental results show the effectiveness of the designed method in detecting brain tumors automatically and produce better accuracy in comparison to previous approaches.
1. E-ISSN: 2321–9637
Volume 2, Issue 1, January 2014
International Journal of Research in Advent Technology
Available Online at: http://www.ijrat.org
311
CLINICAL DECISION MAKING USING
ARTIFICIAL NEURAL NETWORK WITH
PARTICLE SWARM OPTIMIZATION
ALGORITHM
Jayashri S.Kolekar1
, Prof. Chhaya Pawar2
Computer Dept. 1,2
Datta Meghe College of Engg., Airoli, Navi Mumbai 1,2
jaya_kolekar@yahoo.com 1
, chhaya_mdas@yahoo.com 2
ABSTARCT:
Clinical decision making by health professionals is a more complex process The Clinical decision support
system (CDSS) is an interactive decision support system (DSS) Computer Software, which is designed to
assist physicians and other health professionals with decision making tasks, such as determining diagnosis
of patient data. For this work lot of researcher have proposed many algorithms based on cost, efficiency,
time consumption but not in good accuracy. The proposed system based on K-means clustering and
Artificial Neural Network using Particle Swarm Optimization Algorithm will maximize the accuracy and
efficiency of clinical decision-making with minimizing time consumption & cost. Proposed system having
two main operations one is k-means clustering for grouping the patient data according to the symptom
and patient details ,second one is the adaptation of network weights using Particle Swarm Optimization
(PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in
classification of patient dataset. Classification is a machine learning technique used to predict group
membership for data instances.
Keywords: Clinical decision making; Clinical Decision Support System; ANN; PSO; Back propagation; K-
mean.
1. INTRODUCTION
Clinical decision making is the process in which the physician determines patient need what and when. For this
physician gather all the information of the patient and compare it with the knowledge present with him and
prepare the argument for a particular disease state. There may then be no single, right way of applying
diagnostic and therapeutic strategies to a particular case. Clinical decision support system (CDSS) is an
interactive decision support system (DSS) Computer Software, which is designed to assist physicians and other
health professionals with decision making tasks, such as determining diagnosis of patient data. A working
definition has been proposed by Robert Hayward of the Centre for Health Evidence; "Clinical Decision Support
systems link health observations with health knowledge to influence health choices by clinicians for improved
health care". This definition has the advantage of simplifying Clinical Decision Support to a functional concept.
It is a major topic of artificial intelligence in medicine.
There are two types of the Clinical decision support system [3]. Knowledge based CDSS and Non-knowledge
based CDSS.The Knowledge based CDSS as the name implies it contains the knowledge in the form of rules
and evidences .Rules are nothing but the conditional statements .In this the rules are combined with the patient
data we can easily perform the diagnosis and to improve the decision evidences are used.Non-knowledge based
CDSS is without knowledge base which uses artificial intelligence such as machine learning algorithms [4].The
clinical decision making is a very complex process. Today, only fifty percent is the chance of proper diagnosis
of disease and recovery of patient. [2]To improve the diagnosis of disease and accurate treatment we crate
decision support system (DSS) with the use of electronic health record (EHR) with K-means clustering and
Artificial Neural Network using Particle Swarm Optimization Algorithm.
2. E-ISSN: 2321–9637
Volume 2, Issue 1, January 2014
International Journal of Research in Advent Technology
Available Online at: http://www.ijrat.org
312
2. LITERATURE SURVEY
A lot of research work is done in the clinical decision making. The systems such as MYCIN, CASNET, PIP and
Internist-I are proved that the clinical decision support systems performs better than the manual practices.
CASNET (Causal Associational Networks) was developed in early 1960s is a general tool for building expert
system for the diagnosis and treatment of diseases. CASNET major application was the diagnosis and
recommendation of treatment for glaucoma.
In the early 1970s MYCIN was developed to diagnose certain antimicrobial infections and recommends drug
treatment. It was not actually used in practice but research indicated that it proposed an acceptable therapy in about
69% of cases, which was better than the performance of infectious disease experts who were judged using the same
criteria [5].
PIP (Present Illness Program) was developed in 1970s to simulate the behavior of an expert nephrologist in taking
the history of the present illness of a patient with underlying renal disease.
The work on Internist-I in early 1980s was concentrated on the investigation of heuristic methods for imposing
differential diagnostic task structures on clinical decision making. It was applied in diagnoses of internal medicine.
Now a day based on the current needs the CDSS is enhanced. In many systems combination of two techniques are
used to improve the performance of the system.
Casey C. Bennett and Kris Hauser [6], has developed a general purpose model for clinical decision making using
artificial intelligence framework in which the markov decision process with the dynamic decision making is used.
This framework consists of patient agent with the basic information of patient with symptoms and individualized
transition model, and physician agent with beliefs about patient status and treatment effects and has decision making
capabilities. Based on the individualized transition model the MDP is created. Then the evidences are filtered by the
physician. The filtered evidences and the selected data from EHR the MDP search tree is prepared which consists of
number of actions and the treatments from that the optimal action is selected belief state is updated and the cost is
calculated for the current action. Then this process is repeated till the action is equal to no treatment .Then optimal
treatment and cost is calculated. Framework, some of the difficult patient data’s sometimes won’t classify properly
and also decision of treatment will maybe wrong so we are improving the decision making algorithm based on k-
means and artificial neural networks using particle swam optimization leaning algorithm. It improves the accuracy of
giving decision of patient details, efficiency, time consumption and cost of the system.
3. PROPOSED SYSTEM
In this we have patient database which include outcomes, treatment information, demographic information, and
other clinical indicators, was obtained from the electronic health record (EHR). Database is then clustered using
the K-means clustering algorithm based on the symptoms with K= n. This result in n clusters, every cluster
contains the data that are most relevant to specific disease. Each cluster is having the information like disease
name, symptoms, treatment and the cost for it. Then by using artificial neural network with particle swarm
optimization and back propagation we train the system. When the new patient arrives the patient details (basic
information, symptom’s and test results ) are feed to the system the combination of particle swarm optimization
and back propagation finds optimal solution i.e. diseases based on given query with the treatment and cost
associated with it. The general idea of the system is illustrated in the following diagram.
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Figure 1: proposed system for clinical decision making
3.1 K-means algorithm
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group
(called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is
a main task of exploratory data mining, and a common technique for statistical data analysis, used in many fields,
including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics [7]. K-
means [8] (MacQueen, 1967) is one of the simplest unsupervised learning algorithms that solve the well-known
clustering problem. The procedure follows a simple and easy way to classify a given data set through a certain number
of clusters (assume k clusters) fixed a priori
K-mean algorithm is as follows:
1. Select the k points randomly; the points are nothing but the centroid of groups.
2. Based on the centroid the data is partitioned into k groups, the data which is adjacent to it is assigned to that
group.
3. Then recalculate the k centroids after completion of the first partitioning
4. Then iterate the step 2 and 3 till the centroid doesn’t change the location.
Finally, this algorithm aims at minimizing an objective function, in this case a squared error function. The patient
data and the physician data is partitioned using k means algorithm based on the symptoms.in this each group or cluster
consists of disease name, symptoms, treatment and the cost required for it.
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3.2 ANN (Artificial Neural Network)
Artificial neural network is an efficient information processing system which resembles in characteristics with a
biological neural network.in this proposed system we are using feed forward neural network. This type of network has
no recurrent connections between the nodes so the activity flows in one direction.
3.3 Particle swarm optimization with back propagation
The PSO–BP is an optimization algorithm combining the Particle swarm optimization with the
Backpropogation.[9] The particle swarm optimization (PSO) is an evolutionary computation technique
developed by Eberhart and Kennedy in 1995 [10], inspired by social behavior of bird flocking. This technique is
used to find global optimal solution. The Backpropogation (BP) algorithm is used to find local optimal solution
.the combination of both is nothing but the hybrid algorithm is used to find best global optimal solution.
The PSO–BP algorithm’s [11] searching process is started from initializing a group of random particles.
1. Initialize the positions (weight and bias) and velocities of a group of particles randomly.
2. The PSO-BP is trained using the initial particles position.
3. The learning error produced from BP neural network can be treated as particles fitness value according to
initial weight and bias.
4. The learning error at current epoch will be reduced by changing the particles position, which will update the
weight and bias of the network.
(i) The “pbest” value (each particle’s lowest learning error so far) and
(ii) The “gbest” value (lowest learning error found in entire learning process so far) are applied to the
Velocity update to produce a value for positions adjustment to the best solution or targeted learning error.
5. The new sets of positions (NN weight and bias) are produced by adding the calculated velocity value to the
current position value. Then, the new sets of positions are used to produce new learning error in feed
forward NN.
6. This process is repeated until the stopping conditions either minimum learning error or maximum numbers
of iteration are met.
7. The optimization output, which is the solution for the optimization.
4. CONCLUSION
In this paper the proposed system is used to maximize the accuracy of clinical decision by using the feature of
the k-means algorithm for partitioning the database within appropriate groups. The time consumption is also
reduced by the ANN with PSO-BP which is same like APSO for training testing .The proposed system deal with
uncertainty of the clinical decision making and it provides optimal treatment to the patient with complex,
chronic illness.
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CLINICAL DECISION MAKING USING
ARTIFICIAL NEURAL NETWORK WITH
PARTICLE SWARM OPTIMIZATION
ALGORITHM
Jayashri S.Kolekar1
, Prof. Chhaya Pawar2
Computer Dept. 1,2
Datta Meghe College of Engg., Airoli, Navi Mumbai 1,2
jaya_kolekar@yahoo.com 1
, chhaya_mdas@yahoo.com 2
ABSTARCT:
Clinical decision making by health professionals is a more complex process The Clinical decision support
system (CDSS) is an interactive decision support system (DSS) Computer Software, which is designed to
assist physicians and other health professionals with decision making tasks, such as determining diagnosis
of patient data. For this work lot of researcher have proposed many algorithms based on cost, efficiency,
time consumption but not in good accuracy. The proposed system based on K-means clustering and
Artificial Neural Network using Particle Swarm Optimization Algorithm will maximize the accuracy and
efficiency of clinical decision-making with minimizing time consumption & cost. Proposed system having
two main operations one is k-means clustering for grouping the patient data according to the symptom
and patient details ,second one is the adaptation of network weights using Particle Swarm Optimization
(PSO) was proposed as a mechanism to improve the performance of Artificial Neural Network (ANN) in
classification of patient dataset. Classification is a machine learning technique used to predict group
membership for data instances.
Keywords: Clinical decision making; Clinical Decision Support System; ANN; PSO; Back propagation; K-
mean.
1. INTRODUCTION
Clinical decision making is the process in which the physician determines patient need what and when. For this
physician gather all the information of the patient and compare it with the knowledge present with him and
prepare the argument for a particular disease state. There may then be no single, right way of applying
diagnostic and therapeutic strategies to a particular case. Clinical decision support system (CDSS) is an
interactive decision support system (DSS) Computer Software, which is designed to assist physicians and other
health professionals with decision making tasks, such as determining diagnosis of patient data. A working
definition has been proposed by Robert Hayward of the Centre for Health Evidence; "Clinical Decision Support
systems link health observations with health knowledge to influence health choices by clinicians for improved
health care". This definition has the advantage of simplifying Clinical Decision Support to a functional concept.
It is a major topic of artificial intelligence in medicine.
There are two types of the Clinical decision support system [3]. Knowledge based CDSS and Non-knowledge
based CDSS.The Knowledge based CDSS as the name implies it contains the knowledge in the form of rules
and evidences .Rules are nothing but the conditional statements .In this the rules are combined with the patient
data we can easily perform the diagnosis and to improve the decision evidences are used.Non-knowledge based
CDSS is without knowledge base which uses artificial intelligence such as machine learning algorithms [4].The
clinical decision making is a very complex process. Today, only fifty percent is the chance of proper diagnosis
of disease and recovery of patient. [2]To improve the diagnosis of disease and accurate treatment we crate
decision support system (DSS) with the use of electronic health record (EHR) with K-means clustering and
Artificial Neural Network using Particle Swarm Optimization Algorithm.](https://image.slidesharecdn.com/paperid-212014112-140828021154-phpapp01/85/Paper-id-212014112-1-320.jpg)
![E-ISSN: 2321–9637
Volume 2, Issue 1, January 2014
International Journal of Research in Advent Technology
Available Online at: http://www.ijrat.org
312
2. LITERATURE SURVEY
A lot of research work is done in the clinical decision making. The systems such as MYCIN, CASNET, PIP and
Internist-I are proved that the clinical decision support systems performs better than the manual practices.
CASNET (Causal Associational Networks) was developed in early 1960s is a general tool for building expert
system for the diagnosis and treatment of diseases. CASNET major application was the diagnosis and
recommendation of treatment for glaucoma.
In the early 1970s MYCIN was developed to diagnose certain antimicrobial infections and recommends drug
treatment. It was not actually used in practice but research indicated that it proposed an acceptable therapy in about
69% of cases, which was better than the performance of infectious disease experts who were judged using the same
criteria [5].
PIP (Present Illness Program) was developed in 1970s to simulate the behavior of an expert nephrologist in taking
the history of the present illness of a patient with underlying renal disease.
The work on Internist-I in early 1980s was concentrated on the investigation of heuristic methods for imposing
differential diagnostic task structures on clinical decision making. It was applied in diagnoses of internal medicine.
Now a day based on the current needs the CDSS is enhanced. In many systems combination of two techniques are
used to improve the performance of the system.
Casey C. Bennett and Kris Hauser [6], has developed a general purpose model for clinical decision making using
artificial intelligence framework in which the markov decision process with the dynamic decision making is used.
This framework consists of patient agent with the basic information of patient with symptoms and individualized
transition model, and physician agent with beliefs about patient status and treatment effects and has decision making
capabilities. Based on the individualized transition model the MDP is created. Then the evidences are filtered by the
physician. The filtered evidences and the selected data from EHR the MDP search tree is prepared which consists of
number of actions and the treatments from that the optimal action is selected belief state is updated and the cost is
calculated for the current action. Then this process is repeated till the action is equal to no treatment .Then optimal
treatment and cost is calculated. Framework, some of the difficult patient data’s sometimes won’t classify properly
and also decision of treatment will maybe wrong so we are improving the decision making algorithm based on k-
means and artificial neural networks using particle swam optimization leaning algorithm. It improves the accuracy of
giving decision of patient details, efficiency, time consumption and cost of the system.
3. PROPOSED SYSTEM
In this we have patient database which include outcomes, treatment information, demographic information, and
other clinical indicators, was obtained from the electronic health record (EHR). Database is then clustered using
the K-means clustering algorithm based on the symptoms with K= n. This result in n clusters, every cluster
contains the data that are most relevant to specific disease. Each cluster is having the information like disease
name, symptoms, treatment and the cost for it. Then by using artificial neural network with particle swarm
optimization and back propagation we train the system. When the new patient arrives the patient details (basic
information, symptom’s and test results ) are feed to the system the combination of particle swarm optimization
and back propagation finds optimal solution i.e. diseases based on given query with the treatment and cost
associated with it. The general idea of the system is illustrated in the following diagram.](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)