FORECASTING MYOCARDIAL INFARCTION USING MACHINE LEARNING ALGORITHMS

Transcription

1 Volume 118 No , ISSN: (on-line version) url: ijpam.eu FORECASTING MYOCARDIAL INFARCTION USING MACHINE LEARNING ALGORITHMS Anitha Moses, Sathishkumar R, Meghana M, Meghana Raju M, Madhumitha M Professor, Assistant professor, Final year student(s) Department of Computer Science and Engineering Panimalar Engineering College, Chennai anithaarun07@gmail.com, satz.lic@gmail.com, meghanaravi.1229@gmail.com, meghanaraju.sai@gmail.com Abstract: The top-tier disease which makes unanticipated demise for the people in medical field is the myocardial infarction (heart attack). It is very crucial to forecast the disease at a premature phase. Nowadays, Prediction of heart disease is a challenging factor faced by doctors and hospitals. Accuracy of the heart disease prediction plays a vital role. There are various researches going on to accelerate the prediction of heart disease with the help of machine learning algorithms. Machine learning algorithms are used for prediction. The proposed heart disease prediction system utilizes Linear Regression algorithm to predict the chances of heart related diseases for any person. Linear Regression algorithm provides a better accuracy than the other classifications Data Mining algorithms. Heart related real datasets are picked from UCI Machine Learning Repository. The main objective of this paper is predicting the myocardial infarction of a person using linear regression algorithm. The proposed Linear Regression algorithm provides 81% accuracy in heart attack prediction. Keyword: Machine Learning, heart attack, data Mining, Regression 1. Introduction 2. Machine Learning in Heart Attack Data Collection: STEP 1 Data Collection[Collecting Data from UCI Machine Learning Repository] STEP 2 Data Pre-processing[Noise reduction and feature selection] STEP 3 Data Mining [applying Deep Belief Network Algorithm on dataset] STEP 4 Pattern Evaluation[pattern evaluation using performance analysis charts] STEP 5 Discovery of Knowledge[getting percentage of heart diseases problem] Symptoms Symptom ID Symptom (Attribute) Name 1 Chest pain (angina) 2 Shortness of breath 3 Pain, numbness, weakness or coldness in your legs or arms if the blood vessels in those parts of your body are narrowed 4 Pain in the neck, jaw, throat, upper abdomen or back 5 Fluttering in your chest 6 Racing heartbeat (tachycardia) 859

2 7 Slow heartbeat(bradycardia) 8 Lightheadedness 9 Fainting (syncope) or near fainting 10 Breathlessness with exertion or at rest 11 Swelling of the ankles and feet 12 Swelling in your legs 13 Fatigue 14 Irregular heartbeats that feel rapid, pounding or fluttering 15 Fever 16 Swelling in your abdomen 17 Changes in your heart rhythm 18 Dry or persistent cough 19 Skin rashes or unusual spots 3. Literature Survey MIN CHEN et.al [1] entitled Disease Prediction by Machine Learning over Big data from healthcare communities proposed convolution neural network (CNN)-based multimodal disease risk prediction algorithm. Prediction accuracy algorithm reaches 94.8% with a convergence speed which is faster than that of the CNN-based unimodal disease risk prediction algorithm. Meherwar Fatima et.al [2]entitled Survey of Machine Learning Algorithms for Diseases Diagnostic Proposed comparative analysis of different machine learning algorithms. Prediction brings attention towards the suite of machine learning algorithms and tools that are used for the analysis of diseases and decision-making process accordingly. V.Kirubha et.al [3] entitled Survey on Data Mining Algorithms in Disease Prediction proposed Data Mining medical data and prediction analyze the application of data mining inmedical domain and some of the techniques used in diseases prediction. S.B.BHALERAO et.al[4] entitled Survey Of Heart Disease Prediction Based On Data Mining Algorithms proposed Data Mining techniques used and hybrid combination of data mining algorithms are used for the prediction of heart disease, so that identify the algorithms with high accuracy for further research. Prajakta Ghadge et.al[5] entitled Intelligent Heart Attack Prediction System Using Big Data proposed Data Mining,Hadoop and prediction big data infrastructure for both predictive modeling and information extraction. Sunita A Yadwad et.al[6] entitled Prediction of Heart Disease using Hadoop Mapreduce proposed SVM, Naïve Bayes, Prediction, Hadoop, MapReduce and prediction Propose in this paper fares much better than Naive Bayes (NB) classifier and one-by-one SVM classifier. T Rajasekaran, R Tharani priya et.al[7] entitled Heart Attack Prediction for Diabetic s Patient using Data Analytics proposed Weka, J48, Naive Bayes and prediction Reasonable accuracy. Classifiers of this kind can help in early detection of the vulnerability of a diabetic patient to heart disease. Prachi Paliwal, Mahesh Malviya et.al[8] entitled An Efficient Method for Predicting Heart DiseaseProblem Using Fitness Value proposed fitness value, heart disease, attribute, data mining,classifier and prediction method is more accurately classify the recodes as compared to pervious method. Hlaudi Daniel Masethe, Mosima Anna Masethe et.al[9] entitled Prediction of Heart Disease using Classification Algorithms proposed Algorithm, Classification, Diseases, Heart-Attack and prediction The research result shows prediction accuracy of 99%. Data mining enable the health sector to predict patterns in the dataset. Beant Kaur et.al [10] entitled Review on Heart Disease Prediction System using Data Mining Techniques proposed Data mining techniques and prediction by using data mining algorithm given efficient results. Problem Statement: A set of disease-predicting parameters {Ak} k=1,2..n is decided upon based on observed correlation with disease diagnosis (heart diseases in our case), where n is the number of such parameters. We also have a set of N patients {xi} that have been diagnosed with heart disease or not, and the dataset consists of the {Ak(xi)} parameter readings. where {wk}k=1,2..n is a set of weights for each of the n disease-predicting parameters (to be determined by fitting the dataset to the model). The models are aiming to predict the probability that a given patient x will be diagnosed as heart disease, and predict the weights of the features on finding the probability. When 860

3 our data was a small data sets, then we hope that the model will fit the data without the risk of over fitting. 4. Proposed System The goal of this project is to build a model that can predict the probability of heart disease occurrence, based on a combination of features that describess the disease. In order to achieve the goal, we used data sets that were collected by Cleveland Clinic Foundation in Switzerland. The dataset used in this project is part of a database contains 14 features from Cleveland Clinic Foundation for heart disease. The dataset shows different levels of heart disease presence from 1 to 4 and 0 for the absence of the disease. We have 303 rows of people data with 13 continuous observations of different symptoms. When I was doing my master degree in biomedical engineering years ago, I created many models for my thesis projects, which was about drug repurposing, by using data sets to predict or discover new uses for existing drugs. From those days, I saw the success of machine learning in biomedicine domain, which enriched the computational biology field. In this study, we look into different classic machine learning models, and their discoveries in diseases risks. We have developed two algorithms using linear regression and decision trees, on Cleveland dataset. Recall: is the ratio between positives and the number of number of false negatives. recall=tptp+fn recall=tptp+ +FN F-score: is known as the harmonic mean of precision and recall. acc=112(1p+1r)=2prp+racc=112(1p+1r)=2prp+r Problem characteristics in context of our case study: TP = True positive (has heart disease). TN = True negative (has no heart disease). FP = False positive (has no heart disease) FN = False negative (has heart disease). Graphical Representation: the number of correct true positives plus the 5. Results Performance metrics Accuracy: is the ratio between the number of correct predictions and total number of predications. acc=tp+tntp+tn+fp+fnacc=tp+tntp+tn+fp+fn Precision: is the ratio between the number of correct positives and the number of true positives plus the number of false positives. Precision(p)=TPTP+FPPrecision(p)=TPTP+FP 861

4 Implementation Results: The best parameters for model are ('hinge', 'l1', 0.01, 500) The Cross-Validation score = Linear regression SGD Test score: 0.96 Decision tree Decision tree Cross-Validation scores: Mean Decision tree Cross-Validation score = Mean Decision tree Cross-Validation score = The best parameters for model are entropy The Cross-Validation score = Conclusion Heart attack is crucial health problem in human society. Heart disease is the leading cause of death for both men and women. Know the warning signs and symptoms of a heart attack so that you can act fast if you or someone who you know might be having a heart attack. The chances of survival are greater when emergency treatment begins quickly. This paper mainly focuses on the study of Linear Regression Algorithms to predict the Myocardial Infarction (Heart Attack) earlier than expected. This algorithm provides an accuracy of 96.1% on the real datasets that are taken from UCI Machine Learning Repository. The results provided by this paper is very simple to view. Heart Specialists and even Non- Specialists can predict the outcome of a person easily. The prediction accuracy of the system can be improved by using more real-time datasets to this system. [5] Anushya & A. Pethalakshmi A Comparative Study of Fuzzy Classifiers with Genetic On Heart Data. International Conference on Advancement in Engineering Studies & Technology. [6] Mariammal. D, Jayanthi. S, Dr. P. S. K. Patra Major Disease Diagnosis and Treatment Suggestion System Using Data Mining Techniques International Journal of Advanced Research in Computer Science & Technology IJARCST All Rights Reserved 338 Vol. 2 Issue Special 1 Jan-March 2014 ISSN: (Online). [7] V. Manikantan & S. Latha Predicting the Analysis of Heart Disease Symptoms Using Medicinal Data Mining Methods International Journal on Advanced Computer Theory and Engineering (IJACTE). [8] Milan Kumari Comparative Study of Data Mining Classification Methods in Cardiovascular Disease Prediction. [9] Nidhi Bhatla and Kiran Jyoti A Novel Approach for Heart Disease Diagnosis using Data Mining and Fuzzy Logic International Journal of Computer Applications ( ) Volume 54 No.17, September 2012 [10] V.Jaya Rama krishniah D.V.Chandra Sekar and Dr. K. Ramchand H Rao Predicting the Heart Attack Symptoms using Biomedical Data Mining Techniques V Volume 1, No. 3, May 2012 ISSN References [1] S. Sandhiya P. Pavithra and A. Vidhya Novel Approach for Heart Disease verdict Using Data Mining Technique International Journal of Modern Engineering Research (IJMER). [2] Divya Tomar and Sonali Agarwal A survey on Data Mining approaches for Healthcare International Journal of Bio-Science and Bio-Technology. [3] N. Suneetha CH.V. M. K. Hari and V. Sunil Kumar Modified Gini Index Classification: A Case Study Of Heart Disease Dataset (IJCSE) International Journal on Computer Science and Engineering. [4] Sunita Soni O. P. Vyas Using Associative Classifiers for Predictive Analysis in Health Care Data Mining International Journal of Computer Applications ( ) Volume 4 No.5, July

5 863

6 864