Machine learning-based coronary artery disease diagnosis: A comprehensive review.

Classifying data is a common task in Machine learning. Data mining plays an essential role for extracting knowledge from large databases from enterprises operational databases. Data mining in health care is an emerging field of high importance for providing prognosis and a deeper understanding of medical data. Most data mining methods depend on a set of features that define the behaviour of the learning algorithm and directly or indirectly influence the complexity of resulting models. Heart disease is the leading cause of death in the world over the past 10 years. Researches have been using several data mining techniques in the diagnosis of heart disease. Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin, or when the body cannot effectively use the insulin it produces. Most of these systems have successfully employed Machine learning methods such as Naive Bayes and Support Vector Machines for the classification purpose. Support vector machines are a modern technique in the field of machine learning and have been successfully used in different fields of application. Using diabetics’ diagnosis, the system exhibited good accuracy and predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.

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Applying Machine Learning Methods in Diagnosing Heart Disease for Diabetic Patients

Medical professionals need a reliable prediction methodology to diagnose Diabetes. Data mining is the process of analysing data from different perspectives and summarizing it into useful information. The main goal of data mining is to discover new patterns for the users and to interpret the data patterns to provide meaningful and useful information for the users. Data mining is applied to find useful patterns to help in the important tasks of medical diagnosis and treatment. This project aims for mining the relationship in Diabetes data for efficient classification. The data mining methods and techniques will be explored to identify the suitable methods and techniques for efficient classification of Diabetes dataset and in mining useful patterns.

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Application of Data Mining Methods and Techniques for Diabetes Diagnosis

Coronary Artery Disease (CAD) prediction is a very hard and challenging task in the medical field. The early prediction in the medical field especially the cardiovascular sector is one of the virtuosi. The prior studies about the construction of the early prediction model developed an understanding of the recent techniques to find the variation in medical imaging. The prevention of cardiovascular can be fulfilled through a diet chart prepared by the concerned physician after early prediction. Our research paper consists of the prediction of CAD by the proposed algorithm by constructing of pooled area curve (PUC) in the machine learning method. This knowledgebased identification is an important factor for accurate prediction. This significant approach provides a good impact to determine variation in medical images although weak pixels surrounding it. This pooled area construction in our machine learning algorithm is bagging shrinking veins and tissues with the help of clogging and plaque of blood vessels. Besides, the noisy type database is used in this article for better clarity about identifying the classifier. This research article provides the recent adaptive image-based classification techniques and it comparing existing classification methods to predict CAD earlier for a higher accurate value. This proposed method is taking as Journal of Artificial Intelligence and Capsule Networks (2021) Vol.03/ No.01 Pages: 17-33 http://irojournals.com/aicn/ DOI: https://doi.org/10.36548/jaicn.2021.1.002 18 ISSN: 2582-2012 (online) Submitted: 5.01.2021 Revised: 10.02.2021 Accepted: 2.03.2021 Published: 16.03.2021 evidence to diagnosis any heart disease earlier. The decision-making of classified output provides better accurate results in our proposed algorithm.

https://irojournals.com/aicn/V3/I1/02.pdf

Early Prediction of Coronary Artery Disease (CAD) by Machine Learning Method - A Comparative Study

Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020.

objective of our paper is to predict the chances of diabetic patient getting heart disease. In this study, we are applying Naive Bayes data mining classifier technique which produces an optimal prediction model using minimum training set. Data mining is the analysis step of the Knowledge Discovery in Databases process (KDD). Data mining involves use of techniques to find underlying structures and relationships in a large database. Diabetes is a set of related diseases in which body cannot regulate the amount of sugar specifically glucose (hyperglycemia) in the blood. The diagnosis of diseases is a vital role in medical field. Using diabetic"s diagnosis, the proposed system predicts attributes such as age, sex, blood pressure and blood sugar and the chances of a diabetic patient getting a heart disease.

/pdf/diagnosis-of-heart-disease-for-diabetic-patients-using-naive-36tn961kvr.pdf

Diagnosis of Heart Disease for Diabetic Patients using Naive Bayes Method

Data mining is the analysis step of the Knowledge Discovery in Databases process (KDD). While data mining and knowledge discovery in databases are frequently treated as synonyms, data mining is actually part of the knowledge discovery process. Data mining techniques are used to operate on large volumes of data to discover hidden patterns and relationships helpful in decision making. Diabetes is a chronic disease that occurs when the pancreas does not produce enough insulin, or when the body cannot effectively use the insulin it produces. Most of these systems have successfully employed Support Vector Machines for the classification purpose. On the evidence of this we too have used SVM classifier using radial basis function kernel for our experimentation. The results of our proposed system were quite good. The system exhibited good accuracy in predicting the vulnerability of diabetic patients to heart diseases.

/pdf/diagnosing-vulnerability-of-diabetic-patients-to-heart-3qhdeq73nh.pdf

S. K. Srivatsa

Papers

Diagnosis of Heart Disease for Diabetic Patients using Naive Bayes Method

Diagnosing Vulnerability of Diabetic Patients to Heart Diseases using Support Vector Machines