International audienceHeart and other organs are important parts in human body. As per World Health Organisation(WHO)'s statistics, the cause of death in all over world is mostly due to cardiovascular diseases. The reason behind this are sedentary lifestyle which may lead to obesity, increase in cholesterol level, high blood pressure and hypertension. In this paper, by using various data mining techniques, such as Naive Bayes(NB), Decision Tree(DT), Artificial Intelligence (AI), Neural Network (NN) and clustering algorithms such as Association Rules. Support Vector Machine (SVM) and K-NN algorithms are used to extract the Knowledge from the large number of data set. The generated reports help doctors and nurses to identify about disease and their levels with which they can provide a better treatment to the patient. Text Mining is most commonly used mining technique in health care industry. In this paper we compare K-means clustering algorithm with Map Reduce Algorithm's implementation efficiency in parallel and distributed systems

Bagavathy, S.

Bhuvana, M. K.

Gomathy, V.

Murugesan, Monica

Sheeba Rani, S.

Sujatha, K.

English

Archive Ouverte en Sciences de l'Information et de la Communication

HAL Id: hal-02161583
https://hal.archives-ouvertes.fr/hal-02161583
Submitted on 20 Jun 2019
HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.
L’archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d’enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.
Early heart disease detection using data mining
techniques with hadoop map reduce Early Heart Disease
Detection Using Data Mining Techniques with Hadoop
Map Reduce
S. Bagavathy, V. Gomathy, S. Sheeba Rani, Monica Murugesan, K. Sujatha,
M. K. Bhuvana
To cite this version:
S. Bagavathy, V. Gomathy, S. Sheeba Rani, Monica Murugesan, K. Sujatha, et al.. Early heart
disease detection using data mining techniques with hadoop map reduce Early Heart Disease Detection
Using Data Mining Techniques with Hadoop Map Reduce. International Journal of Pure and Applied
Mathematics, Academic Publishing Ltd, 2018, 119 (12), pp.1915-1920. ￿hal-02161583￿
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/325882352
Early heart disease detection using data mining techniques with hadoop map
reduce
Article  in  International Journal of Pure and Applied Mathematics · January 2018
CITATIONS
0
READS
13
6 authors, including:
Some of the authors of this publication are also working on these related projects:
REAL TIME INTRUDER DETECTION USING PASSIVE INFRARED SENSORS View project
power transformer View project
Dr V Gomathy
Sri Krishna College of Engineering and Technology
16 PUBLICATIONS   16 CITATIONS   
SEE PROFILE
All content following this page was uploaded by Sheeba Rani.S on 30 July 2018.
The user has requested enhancement of the downloaded file.
 
 
 
 
 
 
 
 
 
 
Early Heart Disease Detection Using Data Mining Techniques 
with Hadoop Map Reduce  
 
S.Bagavathy1,V.Gomathy2, S.Sheeba Rani3, Sujatha.K4, Bhuvana.M.K5, Monica.Murugesan6 
1,2,3Associate professor ,Department of Electrical and Electronics Engineering 
4Professor,5PG Students,Department of Computer Science and Engineering 
Sri Krishna College of Engineering and Technology 
Kuniyamuthur,Coimbatore-641008. 
 
Abstract 
 Heart and other organs are  important parts in human body. As per World Health 
Organisation(WHO)’s statistics, the cause of death  in all over world  is mostly due to cardiovascular 
diseases. The reason behind this are sedentary lifestyle which may lead to obesity, increase in 
cholesterol level, high blood pressure and hypertension. In this paper, by using various data mining 
techniques, such as Naive Bayes(NB), Decision Tree(DT), Artificial Intelligence (AI), Neural Network 
(NN) and clustering algorithms such as Association Rules. Support Vector Machine (SVM) and K-NN 
algorithms are used to extract the Knowledge from the large number of data set. The generated reports 
help doctors  and nurses to identify about disease and their levels with which they can provide a better 
treatment to the patient. Text Mining is most commonly used mining technique in health care industry. 
In this paper we compare K-means clustering algorithm with Map Reduce Algorithm’s implementation 
efficiency in parallel and distributed systems. 
  
Keywords- Artificial Intelligence(AI), Decision Tree(DT) ,Naive Baye’s(NB), Neural Networks(NN),K-
Means Clustering,World Health Organization(WHO),Map Reduce . 
 
1. Introduction: 
 
 Heart diseases are also known as cardiovascular diseases which occur due to unhealthy 
lifestyle, smoking, alcohol, high intake of fats which may cause strokes, hypertension, high blood 
pressure, diabetics. Sometimes these diseases may also being caused by genetic reasons and some other 
reasons. Symptoms of Heart diseases are Fluttering in chest, Racing heartbeat, Chest pain, Dizziness, 
Light-headedness, Fainting. Sometimes some people may suffer from heart infection which is caused 
due to some virus, bacteria. In the year 2017, WHO estimated that because of cardiovascular diseases , 
almost 17.7 million people were dead . The death percentage is overall 31% and 13% death from overall 
deaths was  due to coronary heart disease and 6.7 million was  due to stroke. Recent days all the people 
from the age group of 25-40 are advised to consult a heart specialist twice a year to know about their 
heart health. For that people were advised to undergo certain test conducted by well equipped heart 
speciality hospitals like  blood  tests, exercise stress test, cardiac catheterization radionuclide 
International Journal of Pure and Applied Mathematics
Volume 119 No. 12 2018, 1915-1920
ISSN: 1314-3395 (on-line version)
url: http://www.ijpam.eu
Special Issue ijpam.eu
1915
ventriculography, chest X-Rays, Magnetic Resonance Imaging (MRI), electro cardiogram, echo 
cardiograph or multiple gated acquisition scanning that results important information. The reports will 
help the doctors to view about the risk level of organs in human body. In this paper we are using 
Artificial Neural Networks and K-mean clustering for disease prediction and provides 100% accuracy by 
comparing Naïve Bayes and Decision Tree. This is cost effective approach for healthcare organizations 
for making analysis of financial and clinical data decisions. Not only the experienced Doctor can do the 
decision , new practitioner also can able to generate the report and make treatment decision based on 
disease stage of  the heart. 
2. Literature Survey: 
           
Rucha Shinde et al  have used k-means algorithm for clustering and Naïve Baye’s algorithm for 
disease prediction. This is done by taking a large number of raw data as input and produces useful 
information from the data. Serialization technique is used to convert the data into bytestream and it is 
stored in database. Input Data are converted into Comma Separated Vector files (CSV) where these 
files contain the attributes of patients (Age,Gender,Blood Pressure, Sugar Level etc) and it is grouped 
by using k-means algorithm and prediction is applied by using Naïve Baye’s algorithm[1]. 
 Kaan Uyara et al have used  Genetic Algorithm which works based on Recurrent Fuzzy Neural 
Networks idea about the overall estimation of heart disease faced by people in the world  for heart 
disease detection which uses overall 297 patient dataset from them  252 was considered as training 
dataset  and 45 was considered as testing dataset   and overall accurate result came out to be 97.78%  
from testing dataset [2]. It uses Root Mean Square Error(RMSE).Accuracy of overall performance is 
analyzed using factors like sensitivity, F-score, probability of miss classification error, precision and 
specificity. A comparative study made between Artificial Neural Network and Genetic Algorithm based 
Recurrence Fuzzy Neural Network and accuracy is more in Genetic Algorithm based Recurrence Fuzzy 
NN[2]. 
          Jyoti Soni et al used Tanagra tool to compare the classifications of  different types of  data mining 
algorithms such as Naive Baye’s, Decision Tree, K-Nearest Neighbour ,Artificial Neural Network and 
Genetic Algorithm. Artificial Neural Network has been implemented in .NET framework. By comparing 
all these algorithm, Decision Tree has more accuracy. Association Rule Mining is also used with one 
additional parameter lift along with support and confidence to get specificity and sensitivity values[3]. 
              G. Purusothaman et al used two data models-single models and combined models,combined 
models are also called as hybrid data models they have used both classification and clustering 
techniques such as K-NN,Decision Tree, SVM,ANN,Association Rule,Hybrid Approach and Naïve Baye’s 
Approach.On comparing both these models hybrid models are best models to be used and Hybrid 
Approach is said to be the best approach to be used as it can provide better result for diagnosis[4]. 
              Mirpouya Mirmozaffari et al have used WEKA tool for analyzing the dataset and discovering 
knowledge and predicting the patterns from dataset. The process is carried out in two steps Multilayer 
filter preprocess and Evaluation in classification. In order to get balanced dataset from imbalanced 
noisy dataset , they applied filters to remove redundant data and noise from the dataset. In Evaluation 
Classification three different steps are carried out:1. Training set 2. Fold cross validation 3. Percentage 
split[5]. 
 Vikas Chaurasia et al used WEKA tool to compare several attributes for prediction of heart 
diseases with by using different types of data mining algorithms like CART(Classification and 
Regression Tree) ,Decision Table and ID3 
Amongst these CART has highest accuracy and ID3 has highest error rate[6]. 
Data Mining Methodology: 
 
      Data Mining is a technique of finding interesting patterns from the existing data as per different 
conditions with which we can make the data into useful information. Here we use patient’s dataset and 
retrieve output as a useful information with which doctor needs to diagnose the patient or not. Since 
raw dataset may contain some noise, hence data mining techniques are followed. Data Pre-processing 
,Transforming then we use Data Mining Techniques like Classification or Clustering out of which we 
will be using K-means Clustering and Evaluate the Result based upon that a knowledge is represented.  
                      
K-Means Clustering Algorithm: 
                 K-means clustering algorithm, which is an unsupervised type of learning algorithm which 
means clustering process is done on data and these data are being categorized into similar groups. The 
procedure is very simple it takes input dataset which contains values of patients training dataset which 
is with respect to attributes like  age, sex, sugar levels etc, based upon these dataset we retrieve some 
information about heart disease and we form     k-clusters with respect to diseases. Since main idea is to 
International Journal of Pure and Applied Mathematics Special Issue
1916
place these clusters in distant manner so we place these clusters within some distance. In next iteration 
we place few clusters in such a way as they are somewhat related to each other. Further iterations we 
proceed calculating k-centroids from previous step and then binding is then between same set of data 
set and nearest centroids. These k-centroids change their locations at every iterations until no more 
calculations are performed. 
Steps performed in K-means clustering algorithm: 
 
1.Assume A={a1,a2,....an} be the attributes of datasets and B={b1,b2,.....bn} be set of centre points. 
 
2. Randomly choose the ci cluster points. 
3.Calculate distance between data points[attributes(a)] and centre points(b) using formula: 
                                         
                                                                    D(V)=∑∑(|ai-bi|)2 
                                                  Where the range is between i=1 to ci. 
 
4. Assign the data point to the cluster points whose distance from the cluster point to the data 
point is minimum of all. 
 
5.Re-calculate the cluster point. 
 
Map Reduce Algorithm:   
Map Reduce Algorithm basically uses parallel programming to process large dataset. It reduces 
issues from distributed and parallel programming, such as network performance, fault tolerance, load 
balancing. It is implemented by open source Apache Hadoop.Map Reduce is programmed using Java for 
high reliability and scalability.  
                    Hadoop Distributed File System(HDFS) comprises of Hbase and Hadoop Map Reduce. It 
analyzes large data.It has two nodes: Master and Slave Nodes. Master Node comprises of Job Tracker 
and Name Node. Slave Node comprises of Task Tracker and Data Node. 
 
 
 
 
 
 
 
 
 
                                                                
                                                     .... 
 
 
 
Fig 1: Components of Hadoop 
 
 
 
 
Steps involved in Map Reduce Algorithm: 
1.Input : Large Dataset is taken. 
2.Splitting: Splits Dataset into number of  small  
3. Mapping : Generates Key-value pairs in each dataset. 
4: Shuffling: Key-Value pairs are separated based on their association. 
5: Reduce: Reduces the Single Key-Value pair  with its count. 
6: Result: Final Result are reduced and stored in database. 
 
     Master Node 
Job 
Tracker 
Name 
Node 
     Slave Node1  
 
Data 
Node 
Task 
Tracker 
     Slave Node2 
 Task 
Tracker 
    
 Data 
Node 
 
     Slave NodeN 
 Task 
Tracker 
    
 Data 
Node 
 
International Journal of Pure and Applied Mathematics Special Issue
1917
 
Fig.2: Working of Map Reduce Algorithm. 
 
Conclusion: 
 In this paper we have studied  various data mining techniques used to obtain information from 
the large dataset. We have studied about K-Means Clustering Algorithm and compared with Map 
Reduce Algorithm for detecting heart diseases. The output accuracy of Map Reduce Algorithm is better 
than K-Means Clustering Algorithm because of dynamic schema and linear scaling. We use Hbase for 
storing resultant data.It has some latency due to batch processing, so in future batch processing is 
reduced then we can get more accurate output on comparing with other data mining techniques.  
 
References: 
 
1. Shinde, R., Arjun, S., Patil, P., & Waghmare, J. (2015). An Intelligent Heart Disease Prediction 
System Using K-Means Clustering and Naïve Bayes Algorithm. IJCSIT) International Journal 
of Computer Science and Information Technologies, 6(1), 637-639.. 
2. Uyar, K., & İlhan, A. (2017). Diagnosis of heart disease using genetic algorithm based trained 
recurrent fuzzy neural networks. Procedia Computer Science, 120, 588-593. 
3. Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011). Predictive data mining for medical diagnosis: 
An overview of heart disease prediction. International Journal of Computer Applications, 17(8), 
43-48.  
4. Krishnamoorthy, Sujatha, Shalini Punithavathani, and Jaya K. Priya. "Extraction of well-
exposed pixels for image fusion with a sub-banding technique for high dynamic range 
images." International Journal of Image and Data Fusion 8.1 (2017): 54-72. 
International Journal of Pure and Applied Mathematics Special Issue
1918
5. Mirmozaffari, M., Alinezhad, A., & Gilanpour, A. (2017). Data Mining Classification Algorithms 
for Heart Disease Prediction. Int'l Journal of Computing, Communications & Instrumentation 
Engg (IJCCIE), 4(1). 
6.  Gomathy.V, Sheeba Rani.S.2018.“Real time intruder detection using passive infrared sensors” 
in Int.Journal of Pure and Applied Mathematics , Volume.118, No.20, 2018, pp 1219-1224 
7.  Rajalakshmi, K., & Nirmala, K. (2016). Heart disease prediction with mapreduce by using 
weighted association classifier and k-means. Indian Journal of Science and Technology, 9(19). 
8.  Zolfaghar, K., Meadem, N., Teredesai, A., Roy, S. B., Chin, S. C., & Muckian, B. (2013, October). 
Big data solutions for predicting risk-of-readmission for congestive heart failure patients. In Big 
Data, 2013 IEEE International Conference on (pp. 64-71). IEEE. 
9.  Nalavade, J., Gavali, M., Gohil, N., & Jamale, S. (2014). Impelling Heart Attack Prediction 
System using Data Mining and Artificial Neural Network. International Journal of Current 
Engineering and Technology, 4(3), 1-5. 
10. Yadwad, P. P. K. S. A., & Tejaswi, V. V. D. L. A Data Mining Technique for Prediction of Heart 
Disease using Hadoop Mapreduce. 
11. Punithavathani, D. Shalini, K. Sujatha, and J. Mark Jain. "Surveillance of anomaly and misuse 
in critical networks to counter insider threats using computational intelligence." Cluster 
Computing 18.1 (2015): 435-451. 
 
 
International Journal of Pure and Applied Mathematics Special Issue
1919
1920
View publication stats


Early heart disease detection using data mining techniques with hadoop map reduce Early Heart Disease Detection Using Data Mining Techniques with Hadoop Map Reduce

https://hal.archives-ouvertes.fr/hal-02161583/file/IJPAM_HEARTDISEASE%20%281%29.pdf

Early heart disease detection using data mining techniques with hadoop map reduce Early Heart Disease Detection Using Data Mining Techniques with Hadoop Map Reduce

Abstract

Similar works

Full text

Available Versions

Archive Ouverte en Sciences de l'Information et de la Communication