Prediction of Cardiac Disease Based on Patient's Symptoms
20 Apr 2018-pp 794-799
TL;DR: This research work which initiated at an early detection of all the probable symptoms and signs which might further lead to detection of cardiac diseases using data collected from previous patients as well as data input received from the user at that particular time is initiated.
Abstract: This research work which initiated at an early detection of all the probable symptoms and signs which might further lead to detection of cardiac diseases using data collected from previous patients as well as data input received from the user at that particular time. Current scenario of health-care data used for surveillance are no longer simply a time building series of aggregate daily counts. Instead, a wealth of proposed spatial as well as temporal demographic, and symptom information is available at the data presented during the time of execution. Our proposed method incorporates all such information that is being used as a classification approach that compares recent healthcare data against data from that particular baseline distribution and hence classifies subgroups of the given data. In addition, the data sample data used is first tested against many types of classifiers and various other proposed test scores have been evaluated. Test best is further chosen to make predictions. This follows a prototype implementation using a python based data mining tool, Orange (version: 0.17.1). The database can be stored in a cloud to centralize it and make access easier.
Citations
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TL;DR: An IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed, which improves the search capability using the Levy flight algorithm and achieves better accuracy than other approaches.
Abstract: The IoT has applications in many areas such as manufacturing, healthcare, and agriculture, to name a few Recently, wearable devices have become popular with wide applications in the health monitoring system which has stimulated the growth of the Internet of Medical Things (IoMT) The IoMT has an important role to play in reducing the mortality rate by the early detection of disease The prediction of heart disease is a key issue in the analysis of clinical dataset The aim of the proposed investigation is to identify the key characteristics of heart disease prediction using machine learning techniques Many studies have focused on heart disease diagnosis, but the accuracy of the findings is low Therefore, to improve prediction accuracy, an IoMT framework for the diagnosis of heart disease using modified salp swarm optimization (MSSO) and an adaptive neuro-fuzzy inference system (ANFIS) is proposed The proposed MSSO-ANFIS improves the search capability using the Levy flight algorithm The regular learning process in ANFIS is dependent on gradient-based learning and has a tendency to become trapped in local minima The learning parameters are optimized utilizing MSSO to provide better results for ANFIS The following information is taken from medical records to predict the risk of heart disease: blood pressure (BP), age, sex, chest pain, cholesterol, blood sugar, etc The heart condition is identified by classifying the received sensor data using MSSO-ANFIS A simulation and analysis is conducted to show that MSSA-ANFIS works well in relation to disease prediction The results of the simulation demonstrate that the MSSO-ANFIS prediction model achieves better accuracy than the other approaches The proposed MSSO-ANFIS prediction model obtains an accuracy of 9945 with a precision of 9654, which is higher than the other approaches
127 citations
TL;DR: The results of the proposed system gives the chances of getting heart disease in advance in terms of percentages and also displays the accuracy level of the above two algorithms.
8 citations
01 Jul 2019
TL;DR: The objective of this research is to build a Machine learning model to predict common diseases based on real symptoms based on the input of symptoms with the dataset of the most commonly exhibited diseases.
Abstract: The general day to day health of a person is vital for the efficient functioning of the human body. Taking certain prominent symptoms and their diseases to build a Machine learning model to predict common diseases based on real symptoms is the objective of this research. With the dataset of the most commonly exhibited diseases, we built a relation to predicting the possible disease based on the input of symptoms. The proposed model utilizes the capability of different Machine learning algorithms combined with text processing to achieve accurate prediction. Text processing has been implemented using Tokenization and, is combined with various algorithms to test the similarities and the outputs. In health industry, it provides several benefits such as pre-emptive detection of diseases, faster diagnosis, medical history for review of patients etc.
6 citations
Cites methods from "Prediction of Cardiac Disease Based..."
...Prabakaran et al.[3] states that research shows a web system using Naïve Bayes to provide answers to complex queries to diagnose heart disease....
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03 Sep 2021
TL;DR: In this article, a machine learning-based classification algorithm is used for the diagnosis of various diseases such as Breast Cancer, which is one of the primary causes of death among women all over the world in the past recent years.
Abstract: Machine Learning based learning algorithms gives the machine the power to learn on its own without being explicitly programmed by a programmer. It helps in automating the task like classification, clustering, etc. which previously required human intervention. Machine Learning-based classification algorithms today are being widely used for the diagnosis of various diseases such as Breast Cancer. Breast Cancer is one of the primary causes of death among women all over the world in the past recent years. In this paper, Random Forest, Logistics Regression, Decision Tree, Naive Bayes and SVM classification algorithms have been implemented. The experiment in this article has been conducted on the most popular Wisconsin Diagnosis Breast Cancer Dataset (WDBC)[l]. The main objective of conducting this experiment is to analyze the correctness and accuracy of classification algorithms used to classify cancer-causing tumours (Malignant) from non-cancerous tumours (Benign) with the highest precision and accuracy. The experiment results show that Random Forest gave the highest accuracy amongst all the classification algorithms.
2 citations
01 Jan 2022
TL;DR: In this article , the authors proposed a predictive system to identify diseases based on symptoms and give best treatment to the patient using text mining and machine learning algorithms, which is helpful for those patients who cannot afford to go to the physician for treatment.
Abstract: As we know that there are many doctors in all around the world, each doctor treats their patients based on their knowledge and skills. So there may be a possibility of misdiagnosis or sometimes rare diseases may be unnoticed by the doctors. It is really very hectic process for most of the doctors as they have to provide ICD-10 CM code after diagnosis. Hence, we have proposed a predictive system to identify diseases based on symptoms and give best treatment to the patient using text mining and machine learning algorithms. Our system identifies the generalized diseases and provides treatment. Our system is helpful for those patients who cannot afford to go to the physician for treatment. Also, our system is useful for doctors as it makes their work easy as we will provide ICD-10 CM code after classifying the disease. Here, in our system, the patient provides unstructured data as an input to the system and the system will determine diseases from the data provided by the system and suggest the best suitable drugs to treat their diseases. Our models consist of classifiers such as Random Forest, Naive Bayes, Support Vector Machine and Logistic Regression machine learning algorithms. Based on the performance of the classifiers we are doing the comparative analysis to know which classifier is more accurate based on their accuracy score.
References
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TL;DR: Healthcare data mining can enable healthcare organizations to predict trends in the patient conditions and their behaviors, which is accomplished by data analysis from different perspectives and discovering connections and relations from seemingly unrelated information.
Abstract: Tendency for data mining application in healthcare today is great, because healthcare sector is rich with information, and data mining is becoming a necessity. Healthcare organizations produce and collect large volumes of information on daily basis. Use of information technologies allows automatization of processes for extraction of data that help to get interesting knowledge and regularities, which means the elimination of manual tasks and easier extraction of data directly from electronic records, transferring onto secure electronic system of medical records which will save lives and reduce the cost of the healthcare services, as well and early discovery of contagious diseases with the advanced collection of data. Data mining can enable healthcare organizations to predict trends in the patient conditions and their behaviors, which is accomplished by data analysis from different perspectives and discovering connections and relations from seemingly unrelated information. Raw data from healthcare organizations are voluminous and heterogeneous. They need to be collected and stored in the organized forms, and their integration enables forming of hospital information system. Healthcare data mining provides countless possibilities for hidden pattern investigation from these data sets. These patterns can be used by physicians to determine diagnoses, prognoses and treatments for patients in healthcare organizations.
151 citations
"Prediction of Cardiac Disease Based..." refers methods in this paper
...weighted associative classifier (WAC) and Naïve Bayes to predict the probability of the number of patients receiving heart attacks [10][16] ....
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TL;DR: The Principal Component Analysis (PCA) method is proposed to use as a first phase for K-means clustering which will simplify the analysis and visualization of multi dimensional data set and a new method to find the initial centroids to make the algorithm more effective and efficient.
Abstract: Due to incredible growth of high dimensional dataset, conventional data base querying methods are inadequate to extract useful information, so researchers nowadays is forced to develop new techniques to meet the raised requirements. Such large expression data gives rise to a number of new computational challenges not only due to the increase in number of data objects but also due to the increase in number of features/attributes. Hence, to improve the efficiency and accuracy of mining task on high dimensional data, the data must be preprocessed by an efficient dimensionality reduction method. Recently cluster analysis is a popularly used data analysis method in number of areas. K-means is a well known partitioning based clustering technique that attempts to find a user specified number of clusters represented by their centroids. But its output is quite sensitive to initial positions of cluster centers. Again, the number of distance calculations increases exponentially with the increase of the dimensionality of the data. Hence, in this paper we proposed to use the Principal Component Analysis (PCA) method as a first phase for K-means clustering which will simplify the analysis and visualization of multi dimensional data set. Here also, we have proposed a new method to find the initial centroids to make the algorithm more effective and efficient. By comparing the result of original and new approach, it was found that the results obtained are more accurate, easy to understand and above all
the time taken to process the data was substantially reduced. Keywords: Cluster analysis, K-means Algorithm, Dimensionality Reduction, Principal Component Analysis, Hybridized K-means algorithm
103 citations
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Abstract: Today, multinational companies and large organizations have operations in many places in the world. Each place of operation may generate large volumes of data. Corporate decision makers require access from all such sources and take strategic decisions. The information and communication technologies have highly used in the industry .The data warehouse is used in the significant business value by improving the effectiveness of managerial decision-making. In an uncertain and highly competitive business environment, the value of strategic information systems such as these are easily recognized however in today’s business environment, efficiency or speed is not the only key for competitiveness. Such tremendous amount of data, in the order of tera- to peta-bytes, has fundamentally changed science and engineering, transforming many disciplines from data-poor to increasingly data-rich, and calling for new, data-intensive methods to conduct research in science and engineering. To analyze this vast amount of data and drawing fruitful conclusions and inferences it needs the special tools called data mining tools. This paper gives overview of the data mining systems and some of its applications in the different fields.
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TL;DR: This paper presents an ensemble clustering-based approach to develop prognostic systems of cancer patients that admits multiple factors and provides a practical and useful tool in outcome prediction ofcancer patients.
Abstract: Accurate prediction of survival rates of cancer patients is often key to stratify patients for prognosis and treatment. Survival prediction is often accomplished by the TNM system that involves only three factors: tumor extent, lymph node involvement, and metastasis. This prediction from the TNM has been limited, because other potential prognostic factors are not used in the system. Based on availability of large cancer datasets, it is possible to establish powerful prediction systems by using machine learning procedures and statistical methods. In this paper, we present an ensemble clustering-based approach to develop prognostic systems of cancer patients. Our method starts with grouping combinations that are formed using levels of factors recorded in the data. The dissimilarity measure between combinations is obtained through a sequence of data partitions produced by multiple use of PAM algorithm. This dissimilarity measure is then used with a hierarchical clustering method in order to find clusters of combinations. Prediction of survival is made simply by using the survival function derived from each cluster. Our approach admits multiple factors and provides a practical and useful tool in outcome prediction of cancer patients. A demonstration of use of the proposed method is given for lung cancer patients.
64 citations
"Prediction of Cardiac Disease Based..." refers methods in this paper
...The experiments that were carried out using these classification based algorithm such as Naïve Bayes [4][7] , Decision Tree, K-NN and Neural Network and these results have proven to be that of Naïve Bayes technique that have performed better than the others when utilised by thee techniques [3][5] ....
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TL;DR: This paper discusses the data analytical tools and data mining techniques to analyze the medical data as well as spatial data to generate efficient clusters on discrete and continuous spatial medical databases.
Abstract: vast amount of hidden data in huge databases has created tremendous interests in the field of data mining. This paper discusses the data analytical tools and data mining techniques to analyze the medical data as well as spatial data. Spatial data mining includes discovery of interesting and useful patterns from spatial databases by grouping the objects into clusters. This study focuses on discrete and continuous spatial medical databases on which clustering techniques are applied and the efficient clusters were formed. The clusters of arbitrary shapes are formed if the data is continuous in nature. Furthermore, this application investigated data mining techniques such as classical clustering and hierarchical clustering on the spatial data set to generate the efficient clusters. The experimental results showed that there are certain facts that are evolved and can not be superficially retrieved from raw data.
54 citations
"Prediction of Cardiac Disease Based..." refers methods in this paper
...The researchers uses K means clustering algorithm on that particular heart disease where the warehouse which relate to extract data relevance to the heart disease, and applies to type MAFIA (Maximal Frequent Item set Algorithm ) algorithms [6] to calculate weightage of the frequent patterns which are probably very significant to heart attack predictions....
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