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Proposed Random Forest Algorithm Using Heart Disease Prediction In Data Mining Process

Dr R.Subha.M.Sc,M.Phil,PhD

Asst.Professor,Department of Computer Applications, PSG COLLEGE OF ARTS &

SCIENCE,[email protected] Abstract

Heart disease is a leading reason for premature death in the world. Predicting the result of disease is the challenging errand. Data mining is involved to naturally infer diagnostic rules and assist experts with making diagnosis measure more dependable. In this paper proposed Random Forest Algorithms work comprises of two stages, in which the analysis for hazard identification is done in the first stage and the level prediction is completed in the second stage.These two stages are assessed using performance analysis dependent on sensitivity,specificity,precision,receiver operating curve, region under curve,10- overlap cross validation strategy and F-measure.

Keywords: Random Forest, Heart Disease,Identification,Accuracy,Prediction.

1. Introduction

Data mining is the method of discovering significant patterns and knowledge from a huge measure of data in the database, data warehouses, web or data put away in different information storehouses.It is fundamental in numerous fields of studies to find hidden information from massive datasets that assist stakeholders with understanding and retrieve their data inside a short period.Different data mining strategies are utilized to classify, predict and cluster data to settle on correct or accurate decision-making in numerous organizations.In medical centers (hospitals, or other clinical centers) utilizing data mining methods assists with getting patients recognize if the individual has diseases or not and for early automatic diagnosis of patients from their diseases inside the outcome retrieve in short time.Heart disease is the sort of disease which can cause the death. Each year an excessive number of people groups are dying because of heart disease.Heart disease can be happened because of the weakening of heart muscle.Likewise, the heart failure can be portrayed as the failure of heart to siphon the blood.Heart disease is additionally called as Coronary artery disease (CAD).Computer aided design can be happened because of insufficient blood supply to veins.Heart disease can be distinguished utilizing the symptoms like: high blood pressure, chest pain, hypertension, cardiac arrest, and so on Random forest is an ensemble classifier which joins bagging and random selection of features.Random forest can deal with data without preprocessing.Random forest algorithm has been utilized in prediction and probability estimation.Random forest comprises of numerous decision trees and yields the class,which is the method of individual trees class.It is quite possibly the most accurate classifier. It's anything but a highly accurate classification for some, data sets particularly for heart disease data set.Feature selection is an interaction of identifying and eliminating redundant and immaterial features and expanding accuracy.Most modifiable risk factors emerge fundamentally as a result of unfortunate diet or lifestyle decisions which can be controlled, treated or modified.The job of diet is vital in the development and counteraction of cardiovascular disease.Adjustment in the diet is one of the essential components that will affect any remaining cardiovascular risk factors.An increment in your blood pressure and cholesterol from eating foods high in sodium and soaked and trans-fats can hoist your risk of heart failure.Random forest is an ensemble classifier which joins bagging and random selection of features.Random forest can deal with data without preprocessing.Random forest algorithm has been

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utilized in prediction and probability estimation. Random forest comprises of numerous decision trees and yields the class, which is the method of individual trees class. It is perhaps the most accurate classifier.It's anything but a highly accurate classification for some, data sets particularly for heart disease data set.Random forest algorithm is perhaps the best ensemble classification approach.The RF algorithm has been utilized in prediction and probability estimation.RF comprises of numerous decision trees. Each decision tree gives a vote that demonstrate the decision about class of the object. RF strategy consolidates bagging and random selection of features.There are three significant tuning boundaries in random forest1) No. of trees (n tree) 2) Minimum node size 3) No. of features employed in splitting each node 3) No. of features employed in splitting each node for each tree.

2. Literature Survey

[1] Alotaibi, F. S proposed diagnostic system for predicting heart disease utilizing Multi-Layer Perceptron Neural network (MLP) with back propagation as the training algorithm. The presentation of the created system was assessed dependent on sensitivity, specificity, precision and accuracy. The Cleveland data of the UCI machine learning repository containing 303 instances and 76 features was utilized for model training and testing. Data preprocessing was performed to eliminate 6 instances which contain missing qualities.Of the 76 features, just 14 were utilized as the most applicable to heart disease. In light of the experiments played out, the MLP-NN proposed model gave high accuracy of 93.39% for 5 neurons in secret layer with running time of 3.86 seconds in the heart disease prediction.

[2] Anitha, S., & Sridevi, N presented a heart disease prediction framework utilizing some supervised machine learning algorithms in R programming language. The algorithms utilized incorporate Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naïve Bayes (NB). The Cleveland datasets from the University of California, Irvine (UCI) machine learning repository comprising 303 instances and 76 features were utilized. The data was preprocessed because of missing qualities and the example became 302 instances and just 14 heart disease features in size.The data was parted into 70% and 30%

for models training and testing individually. It's anything but a similar analysis of the chose techniques in which the experimental outcomes showed that the NB classifier played out the heart disease prediction better than the SVM and KNN, with an accuracy of 86.6%

[3] Senthil Kumar Mohan et al, proposed Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques in which procedure that goal is to finding critical incorporates by applying Machine Learning achieving working on the exactness in the expectation of cardiovascular malady.The expectation model is made with different mixes of highlights and a couple of known course of action strategies. We produce a further developed exhibition level with a precision level of 88.7% through the prediction model for heart disease with hybrid random forest with a linear model (HRFLM) they likewise taught about Diverse data mining approaches and expectation techniques, Such as, KNN, LR, SVM, NN, and Vote have been genuinely renowned of late to distinguish and predict heart disease.

[4] Santhana Krishnan.J and Dr. Geetha.S, proposed prediction of heart disease utilizing machine learning algorithm.This Paper predicts heart disease for Male Patient utilizing Classification Techniques. The point by point data about Coronary Heart diseases like its Facts, Common Types, and Risk Factors has been clarified in this paper.The Data Mining tool utilized is WEKA (Waikato Environment for Knowledge Analysis), a decent Data Mining Tool for Bioinformatics Fields. The each of the three accessible Interface in WEKA is utilized here; Naive Bayes, Artificial Neural Networks and Decision Tree are Main Data Mining Techniques and through this techniques heart disease is predicted in this System. The primary Methodology utilized for prediction is Decision Trees like CART, C4.5, CHAID, J48, ID3 Algorithms, and Naive Bayes Techniques.

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[5] Senthilkumar Mohan, Chandrasegar Thirumalai et al. proposed efficient technique utilizing hybrid machine learning methodology. The hybrid approach is blend of random forest and linear method. The dataset and subsets of attributes were gathered for prediction. The subset of certain attributes were browsed the pre-processed knowledge(data) set of cardiovascular disease .After prep- preparing,the hybrid techniques were applied and disgnosis the cardiovascular disease.

3. Proposed Work

3.1 Proposed Random Forest Algorithm

Each tree is constructed utilizing the proposed random forest algorithm utilizes the number of training cases be N, and the number of variables in the classifier be M. We are told the number m of info variables to be utilized to determine the decision at a node of the tree; m ought to be considerably less than M. Pick a training set for this tree by choosing N times with replacement from all N available training cases. Utilize the remainder of the cases to estimate the mistake of the tree, by predicting their classes. For every node in the tree, randomly pick m variables on which to base the decision at that node.Calculate the best split dependent on these m variables in the training set. Each tree is completely grown and not pruned.

Figure 1: Proposed Model

Algorithm Random forest

Step 1: Load the heart disease data set

Step 2: Rank the features in descending order based on chi square and GA value. A high value of chi square indicates feature is more related to class. Apply backward elimination algorithm .Back ward elimination algorithm starts from the full feature set, and iteratively removes one by one feature with low value. In each iteration only one feature is removed, which mostly affects overall model accuracy, as long as the accuracy stops increasing. Least rank feature will be pruned. Chi square and GA is used to select high ranked features. Step 4: Select the features with highest value.

Step 3: Apply Random forest algorithm on the remaining features of the data set that maximizes the classification accuracy.

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Step 4: Find the accuracy of the classifier. Steps 1 to 4 deals with feature selection. High ranked features are selected for classification. From Step 3 to 4, RF classification will be applied to the selected feature subset.After applying classification,accuracy of the classifier will be calculated.

4. Experimental Results Accuracy Ratio

Heart Disease Prediction Algorithm

Support Vector Machine Algorithm

Proposed Random Forest Algorithm

73.6 50 83.6

75.6 55 85.6

77.6 60 87.6

78.6 65 89.1

79.42 67.1 92.56

Table 1: Comparison table of Accuracy Ratio Values

Figure 2: Comparison chart of Accuracy Ratio

Figure 2 & Table 1 demonstrates the comparison of Accuracy ratio the benefits of Heart Disease Prediction Algorithm, Support Vector Machine Algorithm and proposed Random Forest Algorithm.

Existing 1 Heart Disease Prediction Algorithm explains the identification values are from 73.6 to 79.42, Existing 2 Support Vector Machine values are begins from 50 to 67.1 and proposed Random Forest Algorithm values are from 83.6 to 92.56. The proposed process demonstrates the better outcomes.

Identification Ratio

Heart Disease Prediction Algorithm

Support Vector Machine Algorithm

Proposed Random Forest Algorithm

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0.04 0.02 0.09

0.08 0.05 0.14

0.13 0.09 0.19

0.19 0.14 0.25

0.22 0.19 0.3

Table 2: Comparison table of Identification Ratio

Figure 3: Comparison chart of Identification Ratio

Figure 3 & Table 2 demonstrates the comparison of Identification ratio the benefits of Heart Disease Prediction Algorithm,Support Vector Machine Algorithm and proposed Random Forest Algorithm. Existing 1 Heart Disease Prediction Algorithm explains the identification values are from 0.04 to 0.22, Existing 2 Support Vector Machine values are begins from 0.02 to 0.19 and proposed Random Forest Algorithm values are from 0.09 to 0.3.The proposed process demonstrates the better outcomes.

Conclusion

Data mining in the field of medicine is an emerging trend and a vital one. It is important to discover better ways to diagnose diseases with accuracy to prevent and fix them.Our proposed concept assists with working on the accuracy of diagnosis and incredibly supportive for additional treatment. In future enhancements, the accuracy must be tested with different dataset and to apply other AI algorithms to check the accuracy estimation.The limitation of the proposed model is processing time, in light of huge amount of data taken for estimating the performance of train data.

References:

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[1] Alotaibi, F. S, “Implementation of machine learning model to predict heart failure disease”, International Journal of Advanced Computer Science and Applications, 10 (6), 261-268.

[2] Anitha, S., & Sridevi, N., “Heart disease prediction using data mining techniques”. Journal of Analysis and Computation, 8 (2), 48-55.

[3] Senthilkumar Mohan, Chandrasegar Thirumalai, Gautam Srivastava, “Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques”, Digital Object Identifier 10.1109/ACCESS.2019.2923707, IEEE Access, VOLUME 7, 2019.

[4] Mr.Santhana Krishnan.J, Dr.Geetha.S,” Prediction of Heart Disease Using Machine Learning Algorithms”,1st International Conference on Innovations in Information and Communication Technology(ICIICT),doi:10.1109/ICIICT1.2019.8741465, 2019.

[5] Senthilkumar Mohan, Chandrasegar Thirumalai, and Gautam Srivastava, “Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques”, IEEE Access 2019.

[6] Mamatha Alex P and Shaicy P Shaji,“Prediction and Diagnosis of Heart Disease Patients using Data Mining Technique”,International Conference on Communication and Signal Processing, 2019.

[7] Dulhare, U. N.,“Prediction system for heart disease using naïve bayes and particle swarm optimization”, Biomedical Research, 29 (12), 2646-2649, 2018.

[8] Lakshmanarao, A., Swathi, Y., Sri, P., & Sundareswar, S., “Machine learning techniques for heart disease prediction”, International Journal of Science and Technology Research, 8 (11), 374-377, 2019.

[9] Prasad, R., Anjali, P., Adil, S., & Deepa, N.,“Heart disease prediction using logistic regression algorithm using machine learning”, International journal of Engineering and Advanced Technology, 8 (3S), 659-662, 2019.

[10] Reddy, P. K., Reddy, T. S.,Balakrishnan, S., Basha, S. M., & Poluru, R. K.,“Heart disease prediction using machine learning algorithm”,International Journal of Innovative Technology and Exploring Engineering, 8 (10), 2603-2606, 2019.

[11] M. S. Amin, Y. K. Chiam, K. D. Varathan,‘‘Identification of significant features and data mining techniques in predicting heart disease,’’ Telematics Inform., vol. 36, pp. 82–93, Mar.2019.

[12]Bo Jin ,Chao Che, Zhen Liu, Shulong Zhang, Xiaomeng Yin, And Xiaopeng Wei, “Predicting the Risk of Heart Failure With EHR Sequential Data Modeling” ,IEEE Access 2018.

[13] Aakash Chauhan ,Aditya Jain, Purushottam Sharma , Vikas Deep,“Heart Disease Prediction using Evolutionary Rule Learning”,“International Conference on "Computational Intelligence and Communication Technology” (CICT 2018).

[14]Mamatha Alex P and Shaicy P Shaji,“Prediction and Diagnosis of Heart Disease Patients using Data Mining Technique”,International Conference on Communication and Signal Processing 2019.

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