Conferences by Dr. V . K R I S H N A I A H
© 2024, IJCSE All Rights Reserved 8 International Journal of Computer Sciences and Engineering, 2024
Now a days, based on different reasons heart diseases are increasing rapidly. If we find out or i... more Now a days, based on different reasons heart diseases are increasing rapidly. If we find out or identify the heart diseases in human beings at an early stage, it is easy to prevent the disease and help the patients. Even though cardiologists and health centers gather relevant data and information every day, but, not applying the knowledge of machine learning algorithms to retrieve valuable of prediction. The main objective of this research is to predict and classify heart diseases by using proposed convolutional neural network classifier. In this classification of evaluation process, feed forward process and back propagation methods will be applied in between the hidden layers. Due to this, the proposed CNN classifier gives best accuracy. By applying this trained classifier has identified the given data, which are either normal or abnormal. So, the entire research has been implemented in Python which produced good results.
Springer International Publishing Switzerland, 2015
Data mining technique in the history of medical data found with enormous investigations resulted ... more Data mining technique in the history of medical data found with enormous investigations resulted that the prediction of heart disease is very important in medical science. The data from medical history has been found as heterogeneous data and it seems that the various forms of data should be interpreted to predict the heart disease of a patient. Various techniques in Data Mining have been applied to predict the patients of heart disease. But, the uncertainty in data was not removed with the techniques available in data mining. To remove uncertainty, it has been made an attempt by introducing fuzziness in the measured data. A membership function was designed and incorporated with the measured value to remove uncertainty. Further, an attempt was made to classify the patients based on the attributes collected from medical field. Minimum distance K-NN classifier was incorporated to classify the data among various groups. It was found that Fuzzy K-NN classifier suits well as compared with other classifiers of parametric techniques.
Conferences / Workshops / Webinars by Dr. V . K R I S H N A I A H
Papers by Dr. V . K R I S H N A I A H
International Journal of Scientific & Engineering Research, 2022
Fuzzy-Rough approaches can play a significant role in data mining, since they give understandable... more Fuzzy-Rough approaches can play a significant role in data mining, since they give understandable outcome. In addition, the approaches considered in data mining contain mostly be learning at greatly ordered and accurate data. In this work, the performance is done of three classifiers using heart disease data prediction system. The fusion of K-NN, Fuzzy K-NN, and Fuzzy-Rough (FRNN) were used to calculate the accuracy of event of heart disease data sets. The experiments are passed out using heart disease data set of Uel machine learning repository, and it is implemented on through the process of using the fuzzy-rough tools in Python and experimenter.
Journal of Advanced Research in Dynamical and Control Systems , 2017
In data mining systems, more prominent objective is the prediction and classification
(P&C) of m... more In data mining systems, more prominent objective is the prediction and classification
(P&C) of medical data like heart disease data. Heart disease data obtained from patient
records will be heterogeneous in nature and of various forms. Since, a perfect data processing
system should preserve privacy in patient’s details makes a system necessity to include a data
security mechanism. For data P&C many existing techniques like K- Nearest Neighbor
(KNN), Fuzzy K-Nearest Neighbor (FKNN) etc., are implemented to classify but they cannot
be able to avoid their uncertainty in the prediction of results with any security. In order to
resolve this objective, the proposed methodology Classification Oriented Condensed Nearest
Neighbor (Co-Cnn) rule is chosen for data reduction and Convolutional Neural Networks (CNN) is chosen for P&C with Elliptic Curve Digital Signature Algorithm (ECDSA) and
implemented to provide an efficient data secured P&C of heart disease.
International Journal of Computer Science and Information Technologies, 2013
Cancer is the most important cause of death for
both men and women. The early detection of cance... more Cancer is the most important cause of death for
both men and women. The early detection of cancer can be
helpful in curing the disease completely. So the requirement of
techniques to detect the occurrence of cancer nodule in early
stage is increasing. A disease that is commonly misdiagnosed is
lung cancer. Earlier diagnosis of Lung Cancer saves enormous
lives, failing which may lead to other severe problems causing
sudden fatal end. Its cure rate and prediction depends mainly
on the early detection and diagnosis of the disease. One of the
most common forms of medical malpractices globally is an
error in diagnosis. 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. In
this study, we briefly examine the potential use of classification
based data mining techniques such as Rule based, Decision
tree, Naïve Bayes and Artificial Neural Network to massive
volume of healthcare data. The healthcare industry collects
huge amounts of healthcare data which, unfortunately, are not
“mined” to discover hidden information. For data
preprocessing and effective decision making One Dependency
Augmented Naïve Bayes classifier (ODANB) and naive creedal
classifier 2 (NCC2) are used. This is an extension of naïve
Bayes to imprecise probabilities that aims at delivering robust
classifications also when dealing with small or incomplete data
sets. Discovery of hidden patterns and relationships often goes
unexploited. Diagnosis of Lung Cancer Disease can answer
complex “what if” queries which traditional decision support
systems cannot. Using generic lung cancer symptoms such as
age, sex, Wheezing, Shortness of breath, Pain in shoulder,
chest, arm, it can predict the likelihood of patients getting a
lung cancer disease. Aim of the paper is to propose a model for
early detection and correct diagnosis of the disease which will
help the doctor in saving the life of the patient
International Journal of Computer Science Engineering and Information Technology Research, 2013
This paper can present an overview of the applications of data mining techniques, medical, resear... more This paper can present an overview of the applications of data mining techniques, medical, research, and
educational aspects of Clinical Predictions. In medical and health care areas, due to regulations and due to the availability
of computers, a large amount of data is becoming available. On the one hand, practitioners are expected to use all this data
in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make
diagnosis, prognosis and treatment schedules. A major objective of this paper is to evaluate data mining techniques in
clinical and health care applications to develop a accurate decisions. The paper also provides a detailed discussion of
medical data mining techniques can improve various aspects of Clinical Predictions.
International Journal of Computer Science International Journal of Computer Sciencesand Engineering , 2014
Classification in data mining is a technique based on machine learning algorithms which uses math... more Classification in data mining is a technique based on machine learning algorithms which uses mathematics, statistics,
probability distributions and artificial intelligence... To predict group membership for data items or to represent descriptive
analysis of data items for effective decision making .Now a day’s data mining is touching every aspect of individual life includes
Data Mining for Financial Data Analysis, Data Mining for the Telecommunications Industry Data Analysis, Data Mining for the
Retail Industry Data Analysis, Data Mining in Healthcare and Biomedical Research Data Analysis, and Data Mining in Science
and Engineering Data Analysis, etc. The goal of this survey is to provide a comprehensive review of different classification
techniques in data mining based on decision tree, rule based Algorithms, neural networks, support vector machines, Bayesian
networks, and Genetic Algorithms and Fuzzy logic.
International Journal of Computer Applications, 2016
The Healthcare trade usually clinical diagnosis is ended
typically by doctor’s knowledge and pra... more The Healthcare trade usually clinical diagnosis is ended
typically by doctor’s knowledge and practice. Computer
Aided Decision Support System plays a major task in medical
field. Data mining provides the methodology and technology
to alter these mounds of data into useful information for
decision making. By using data mining techniques it takes less
time for the prediction of the disease with more accuracy.
Among the increasing research on heart disease predicting
system, it has happened to significant to categories the
research outcomes and gives readers with an outline of the
existing heart disease prediction techniques in each category.
Data mining tools can answer trade questions that
conventionally in use much time overriding to decide. In this
paper we study different papers in which one or more
algorithms of data mining used for the prediction of heart
disease. As of the study it is observed that Fuzzy Intelligent
Techniques increase the accuracy of the heart disease
prediction system. The generally used techniques for Heart
Disease Prediction and their complexities are summarized in
this paper
CVR Journal of Science and Technology, 2013
This paper aim is to average the use of techniques of decision trees, in combination with the man... more This paper aim is to average the use of techniques of decision trees, in combination with the management model CRISP-ADM, to help in the prediction of heart diseases. It is widely based on decision trees, an important concept in the field of artificial intelligence. This paper focuse on discussing how these trees are able to assist in the result making process of identifying heart diseases by the analysis of information provided from the hospitals. This information is captured with the help of techniques and the CRISP-DM management model of data mining in large prepared databases logged from hospital day to day transactions.
Conference Presentations by Dr. V . K R I S H N A I A H
IEEE Xplore Digital Library, 2014
Data mining technique in the history of medical
data found with enormous investigations found th... more Data mining technique in the history of medical
data found with enormous investigations found that the
prediction of heart disease is very important in medical science.
In medical history it is observed that the unstructured data as
heterogeneous data and it is observed that the data formed with
different attributes should be analyzed to predict and provide
information for making diagnosis of a heart patient. Various
techniques in Data Mining have been applied to predict the heart
disease patients. But, the uncertainty in data was not removed
with the techniques available in data mining and implemented by
various authors. To remove uncertainty of unstructured data, an
attempt was made by introducing fuzziness in the measured data.
A membership function was designed and incorporated with the
measured value to remove uncertainty and fuzzified data was
used to predict the heart disease patients.. Further, an attempt
was made to classify the patients based on the attributes collected
from medical field. Minimum Euclidean distance Fuzzy K-NN
classifier was designed to classify the training and testing data
belonging to different classes. It was found that Fuzzy K-NN
classifier suits well as compared with other classifiers of
parametric techniques.
Uploads
Conferences by Dr. V . K R I S H N A I A H
Conferences / Workshops / Webinars by Dr. V . K R I S H N A I A H
Papers by Dr. V . K R I S H N A I A H
(P&C) of medical data like heart disease data. Heart disease data obtained from patient
records will be heterogeneous in nature and of various forms. Since, a perfect data processing
system should preserve privacy in patient’s details makes a system necessity to include a data
security mechanism. For data P&C many existing techniques like K- Nearest Neighbor
(KNN), Fuzzy K-Nearest Neighbor (FKNN) etc., are implemented to classify but they cannot
be able to avoid their uncertainty in the prediction of results with any security. In order to
resolve this objective, the proposed methodology Classification Oriented Condensed Nearest
Neighbor (Co-Cnn) rule is chosen for data reduction and Convolutional Neural Networks (CNN) is chosen for P&C with Elliptic Curve Digital Signature Algorithm (ECDSA) and
implemented to provide an efficient data secured P&C of heart disease.
both men and women. The early detection of cancer can be
helpful in curing the disease completely. So the requirement of
techniques to detect the occurrence of cancer nodule in early
stage is increasing. A disease that is commonly misdiagnosed is
lung cancer. Earlier diagnosis of Lung Cancer saves enormous
lives, failing which may lead to other severe problems causing
sudden fatal end. Its cure rate and prediction depends mainly
on the early detection and diagnosis of the disease. One of the
most common forms of medical malpractices globally is an
error in diagnosis. 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. In
this study, we briefly examine the potential use of classification
based data mining techniques such as Rule based, Decision
tree, Naïve Bayes and Artificial Neural Network to massive
volume of healthcare data. The healthcare industry collects
huge amounts of healthcare data which, unfortunately, are not
“mined” to discover hidden information. For data
preprocessing and effective decision making One Dependency
Augmented Naïve Bayes classifier (ODANB) and naive creedal
classifier 2 (NCC2) are used. This is an extension of naïve
Bayes to imprecise probabilities that aims at delivering robust
classifications also when dealing with small or incomplete data
sets. Discovery of hidden patterns and relationships often goes
unexploited. Diagnosis of Lung Cancer Disease can answer
complex “what if” queries which traditional decision support
systems cannot. Using generic lung cancer symptoms such as
age, sex, Wheezing, Shortness of breath, Pain in shoulder,
chest, arm, it can predict the likelihood of patients getting a
lung cancer disease. Aim of the paper is to propose a model for
early detection and correct diagnosis of the disease which will
help the doctor in saving the life of the patient
educational aspects of Clinical Predictions. In medical and health care areas, due to regulations and due to the availability
of computers, a large amount of data is becoming available. On the one hand, practitioners are expected to use all this data
in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make
diagnosis, prognosis and treatment schedules. A major objective of this paper is to evaluate data mining techniques in
clinical and health care applications to develop a accurate decisions. The paper also provides a detailed discussion of
medical data mining techniques can improve various aspects of Clinical Predictions.
probability distributions and artificial intelligence... To predict group membership for data items or to represent descriptive
analysis of data items for effective decision making .Now a day’s data mining is touching every aspect of individual life includes
Data Mining for Financial Data Analysis, Data Mining for the Telecommunications Industry Data Analysis, Data Mining for the
Retail Industry Data Analysis, Data Mining in Healthcare and Biomedical Research Data Analysis, and Data Mining in Science
and Engineering Data Analysis, etc. The goal of this survey is to provide a comprehensive review of different classification
techniques in data mining based on decision tree, rule based Algorithms, neural networks, support vector machines, Bayesian
networks, and Genetic Algorithms and Fuzzy logic.
typically by doctor’s knowledge and practice. Computer
Aided Decision Support System plays a major task in medical
field. Data mining provides the methodology and technology
to alter these mounds of data into useful information for
decision making. By using data mining techniques it takes less
time for the prediction of the disease with more accuracy.
Among the increasing research on heart disease predicting
system, it has happened to significant to categories the
research outcomes and gives readers with an outline of the
existing heart disease prediction techniques in each category.
Data mining tools can answer trade questions that
conventionally in use much time overriding to decide. In this
paper we study different papers in which one or more
algorithms of data mining used for the prediction of heart
disease. As of the study it is observed that Fuzzy Intelligent
Techniques increase the accuracy of the heart disease
prediction system. The generally used techniques for Heart
Disease Prediction and their complexities are summarized in
this paper
Conference Presentations by Dr. V . K R I S H N A I A H
data found with enormous investigations found that the
prediction of heart disease is very important in medical science.
In medical history it is observed that the unstructured data as
heterogeneous data and it is observed that the data formed with
different attributes should be analyzed to predict and provide
information for making diagnosis of a heart patient. Various
techniques in Data Mining have been applied to predict the heart
disease patients. But, the uncertainty in data was not removed
with the techniques available in data mining and implemented by
various authors. To remove uncertainty of unstructured data, an
attempt was made by introducing fuzziness in the measured data.
A membership function was designed and incorporated with the
measured value to remove uncertainty and fuzzified data was
used to predict the heart disease patients.. Further, an attempt
was made to classify the patients based on the attributes collected
from medical field. Minimum Euclidean distance Fuzzy K-NN
classifier was designed to classify the training and testing data
belonging to different classes. It was found that Fuzzy K-NN
classifier suits well as compared with other classifiers of
parametric techniques.
(P&C) of medical data like heart disease data. Heart disease data obtained from patient
records will be heterogeneous in nature and of various forms. Since, a perfect data processing
system should preserve privacy in patient’s details makes a system necessity to include a data
security mechanism. For data P&C many existing techniques like K- Nearest Neighbor
(KNN), Fuzzy K-Nearest Neighbor (FKNN) etc., are implemented to classify but they cannot
be able to avoid their uncertainty in the prediction of results with any security. In order to
resolve this objective, the proposed methodology Classification Oriented Condensed Nearest
Neighbor (Co-Cnn) rule is chosen for data reduction and Convolutional Neural Networks (CNN) is chosen for P&C with Elliptic Curve Digital Signature Algorithm (ECDSA) and
implemented to provide an efficient data secured P&C of heart disease.
both men and women. The early detection of cancer can be
helpful in curing the disease completely. So the requirement of
techniques to detect the occurrence of cancer nodule in early
stage is increasing. A disease that is commonly misdiagnosed is
lung cancer. Earlier diagnosis of Lung Cancer saves enormous
lives, failing which may lead to other severe problems causing
sudden fatal end. Its cure rate and prediction depends mainly
on the early detection and diagnosis of the disease. One of the
most common forms of medical malpractices globally is an
error in diagnosis. 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. In
this study, we briefly examine the potential use of classification
based data mining techniques such as Rule based, Decision
tree, Naïve Bayes and Artificial Neural Network to massive
volume of healthcare data. The healthcare industry collects
huge amounts of healthcare data which, unfortunately, are not
“mined” to discover hidden information. For data
preprocessing and effective decision making One Dependency
Augmented Naïve Bayes classifier (ODANB) and naive creedal
classifier 2 (NCC2) are used. This is an extension of naïve
Bayes to imprecise probabilities that aims at delivering robust
classifications also when dealing with small or incomplete data
sets. Discovery of hidden patterns and relationships often goes
unexploited. Diagnosis of Lung Cancer Disease can answer
complex “what if” queries which traditional decision support
systems cannot. Using generic lung cancer symptoms such as
age, sex, Wheezing, Shortness of breath, Pain in shoulder,
chest, arm, it can predict the likelihood of patients getting a
lung cancer disease. Aim of the paper is to propose a model for
early detection and correct diagnosis of the disease which will
help the doctor in saving the life of the patient
educational aspects of Clinical Predictions. In medical and health care areas, due to regulations and due to the availability
of computers, a large amount of data is becoming available. On the one hand, practitioners are expected to use all this data
in their work but, at the same time, such a large amount of data cannot be processed by humans in a short time to make
diagnosis, prognosis and treatment schedules. A major objective of this paper is to evaluate data mining techniques in
clinical and health care applications to develop a accurate decisions. The paper also provides a detailed discussion of
medical data mining techniques can improve various aspects of Clinical Predictions.
probability distributions and artificial intelligence... To predict group membership for data items or to represent descriptive
analysis of data items for effective decision making .Now a day’s data mining is touching every aspect of individual life includes
Data Mining for Financial Data Analysis, Data Mining for the Telecommunications Industry Data Analysis, Data Mining for the
Retail Industry Data Analysis, Data Mining in Healthcare and Biomedical Research Data Analysis, and Data Mining in Science
and Engineering Data Analysis, etc. The goal of this survey is to provide a comprehensive review of different classification
techniques in data mining based on decision tree, rule based Algorithms, neural networks, support vector machines, Bayesian
networks, and Genetic Algorithms and Fuzzy logic.
typically by doctor’s knowledge and practice. Computer
Aided Decision Support System plays a major task in medical
field. Data mining provides the methodology and technology
to alter these mounds of data into useful information for
decision making. By using data mining techniques it takes less
time for the prediction of the disease with more accuracy.
Among the increasing research on heart disease predicting
system, it has happened to significant to categories the
research outcomes and gives readers with an outline of the
existing heart disease prediction techniques in each category.
Data mining tools can answer trade questions that
conventionally in use much time overriding to decide. In this
paper we study different papers in which one or more
algorithms of data mining used for the prediction of heart
disease. As of the study it is observed that Fuzzy Intelligent
Techniques increase the accuracy of the heart disease
prediction system. The generally used techniques for Heart
Disease Prediction and their complexities are summarized in
this paper
data found with enormous investigations found that the
prediction of heart disease is very important in medical science.
In medical history it is observed that the unstructured data as
heterogeneous data and it is observed that the data formed with
different attributes should be analyzed to predict and provide
information for making diagnosis of a heart patient. Various
techniques in Data Mining have been applied to predict the heart
disease patients. But, the uncertainty in data was not removed
with the techniques available in data mining and implemented by
various authors. To remove uncertainty of unstructured data, an
attempt was made by introducing fuzziness in the measured data.
A membership function was designed and incorporated with the
measured value to remove uncertainty and fuzzified data was
used to predict the heart disease patients.. Further, an attempt
was made to classify the patients based on the attributes collected
from medical field. Minimum Euclidean distance Fuzzy K-NN
classifier was designed to classify the training and testing data
belonging to different classes. It was found that Fuzzy K-NN
classifier suits well as compared with other classifiers of
parametric techniques.