Role of machine learning algorithms over heart diseases prediction

2020 ◽  
Author(s):  
Siva Kumar Jonnavithula ◽  
Abhilash Kumar Jha ◽  
Modepalli Kavitha ◽  
Singaraju Srinivasulu
Keyword(s):  
Machine Learning ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms

scholarly journals An analytical survey on the role of machine learning algorithms in case of intrusion detection

2020 ◽  
Vol 5 (19) ◽  
pp. 32-35
Author(s):  
Anand Vijay ◽  
Kailash Patidar ◽  
Manoj Yadav ◽  
Rishi Kushwah
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Handling Mechanism ◽  
The Impact

In this paper an analytical survey on the role of machine learning algorithms in case of intrusion detection has been presented and discussed. This paper shows the analytical aspects in the development of efficient intrusion detection system (IDS). The related study for the development of this system has been presented in terms of computational methods. The discussed methods are data mining, artificial intelligence and machine learning. It has been discussed along with the attack parameters and attack types. This paper also elaborates the impact of different attack and handling mechanism based on the previous papers.

The Model Selection Curse

2019 ◽  
Vol 1 (2) ◽  
pp. 127-140 ◽  
Author(s):  
Kfir Eliaz ◽  
Ran Spiegler
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Learning Algorithms ◽  
Personal Data ◽  
Estimation Procedure ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Truth Telling ◽  
Agent Based

A statistician takes an action on behalf of an agent, based on the agent’s self-reported personal data and a sample involving other people. The action that he takes is an estimated function of the agent’s report. The estimation procedure involves model selection. We ask the following question: Is truth-telling optimal for the agent given the statistician’s procedure? We analyze this question in the context of a simple example that highlights the role of model selection. We suggest that our simple exercise may have implications for the broader issue of human interaction with machine learning algorithms. (JEL C52)

scholarly journals Prediction of Metabolic Syndrome in a Mexican Population Applying Machine Learning Algorithms

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 581
Author(s):  
Guadalupe Obdulia Gutiérrez-Esparza ◽  
Oscar Infante Vázquez ◽  
Maite Vallejo ◽  
José Hernández-Torruco
Keyword(s):  
Machine Learning ◽  
Metabolic Syndrome ◽  
Abdominal Obesity ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Mexican Population ◽  
World Health ◽  
Prognostic Variables ◽  
Data Set

Metabolic syndrome is a health condition that increases the risk of heart diseases, diabetes, and stroke. The prognostic variables that identify this syndrome have already been defined by the World Health Organization (WHO), the National Cholesterol Education Program Third Adult Treatment Panel (ATP III) as well as by the International Diabetes Federation. According to these guides, there is some symmetry among anthropometric prognostic variables to classify abdominal obesity in people with metabolic syndrome. However, some appear to be more sensitive than others, nevertheless, these proposed definitions have failed to appropriately classify a specific population or ethnic group. In this work, we used the ATP III criteria as the framework with the purpose to rank the health parameters (clinical and anthropometric measurements, lifestyle data, and blood tests) from a data set of 2942 participants of Mexico City Tlalpan 2020 cohort, applying machine learning algorithms. We aimed to find the most appropriate prognostic variables to classify Mexicans with metabolic syndrome. The criteria of sensitivity, specificity, and balanced accuracy were used for validation. The ATP III using Waist-to-Height-Ratio (WHtR) as an anthropometric index for the diagnosis of abdominal obesity achieved better performance in classification than waist or body mass index. Further work is needed to assess its precision as a classification tool for Metabolic Syndrome in a Mexican population.

scholarly journals Heart Disease Prediction Using Machine Learning

2021 ◽  
pp. 267-276
Author(s):  
Baban. U. Rindhe ◽  
Nikita Ahire ◽  
Rupali Patil ◽  
Shweta Gagare ◽  
Manisha Darade
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Heart Disease ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Whole Body ◽  
Support Vector ◽  
Learning Techniques

Heart-related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need fora reliable, accurate, and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals in the diagnosis of heart-related diseases. Heart is the next major organ comparing to the brain which has more priority in the Human body. It pumps the blood and supplies it to all organs of the whole body. Prediction of occurrences of heart diseases in the medical field is significant work. Data analytics is useful for prediction from more information and it helps the medical center to predict various diseases. A huge amount of patient-related data is maintained on monthly basis. The stored data can be useful for the source of predicting the occurrence of future diseases. Some of the data mining and machine learning techniques are used to predict heart diseases, such as Artificial Neural Network (ANN), Random Forest,and Support Vector Machine (SVM).Prediction and diagnosingof heart disease become a challenging factor faced by doctors and hospitals both in India and abroad. To reduce the large scale of deaths from heart diseases, a quick and efficient detection technique is to be discovered. Data mining techniques and machine learning algorithms play a very important role in this area. The researchers accelerating their research works to develop software with thehelp of machine learning algorithms which can help doctors to decide both prediction and diagnosing of heart disease. The main objective of this research project is to predict the heart disease of a patient using machine learning algorithms.

Prediction Models for Healthcare Using Machine Learning

2021 ◽  
pp. 70-91
Author(s):  
Ayushe Gangal ◽  
Peeyush Kumar ◽  
Sunita Kumari ◽  
Anu Saini
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Brain Diseases ◽  
Health Issues ◽  
Industry Study ◽  
Learning Techniques

Healthcare is always a sensitive issue for all of us, and it will always remain. Predicting various types of health issues in advance can lead us to a better life. Various types of health problems are there like cancer, heart diseases, diabetes, arthritis, pneumonia, lungs disease, liver disease, and brain disease, which all are at high risk. To reduce the risk of health issues, some suitable models are needed for prediction. Thus, it became as a motivational factor for the authors to survey the existing literature on this topic thoroughly and have consequently to identify suitable machine learning techniques so that improvement can be possible while selecting a prediction model. In this chapter, concept of survey is used to provide the prediction models for healthcare issues along with the challenges associated with each model. This chapter will broadly cover the following: machine learning algorithms used in health industry, study various prediction models for Cancer, Heart diseases, Diabetes and Brain diseases, comparative study of various machine learning algorithms used for prediction.

A Novel Ensemble Stacking Classification of Genetic Variations Using Machine Learning Algorithms

2021 ◽  
Author(s):  
Jahnavi Yeturu ◽  
Poongothai Elango ◽  
S. P. Raja ◽  
P. Nagendra Kumar
Keyword(s):  
Machine Learning ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Genetic Mutation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Genetic Mutations ◽  
Validation Data ◽  
Data Set

Genetics is the clinical review of congenital mutation, where the principal advantage of analyzing genetic mutation of humans is the exploration, analysis, interpretation and description of the genetic transmitted and inherited effect of several diseases such as cancer, diabetes and heart diseases. Cancer is the most troublesome and disordered affliction as the proportion of cancer sufferers is growing massively. Identification and discrimination of the mutations that impart to the enlargement of tumor from the unbiased mutations is difficult, as majority tumors of cancer are able to exercise genetic mutations. The genetic mutations are systematized and categorized to sort the cancer by way of medical observations and considering clinical studies. At the present time, genetic mutations are being annotated and these interpretations are being accomplished either manually or using the existing primary algorithms. Evaluation and classification of each and every individual genetic mutation was basically predicated on evidence from documented content built on medical literature. Consequently, as a means to build genetic mutations, basically, depending on the clinical evidences persists a challenging task. There exist various algorithms such as one hot encoding technique is used to derive features from genes and their variations, TF-IDF is used to extract features from the clinical text data. In order to increase the accuracy of the classification, machine learning algorithms such as support vector machine, logistic regression, Naive Bayes, etc., are experimented. A stacking model classifier has been developed to increase the accuracy. The proposed stacking model classifier has obtained the log loss 0.8436 and 0.8572 for cross-validation data set and test data set, respectively. By the experimentation, it has been proved that the proposed stacking model classifier outperforms the existing algorithms in terms of log loss. Basically, minimum log loss refers to the efficient model. Here the log loss has been reduced to less than 1 by using the proposed stacking model classifier. The performance of these algorithms can be gauged on the basis of the various measures like multi-class log loss.

scholarly journals Review Paper on Prediction of Heart Disease using Machine Learning Algorithms

2021 ◽  
Vol 9 (VI) ◽  
pp. 2937-2940
Author(s):  
Aadar Pandita
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Review Paper ◽  
Heart Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The World ◽  
Medical Researchers ◽  
High Level ◽  
The Impact

: Heart disease has been one of the ruling causes for death for quite some time now. About 31% of all deaths every year in the world take place as a result of cardiovascular diseases [1]. A majority of the patients remain uninformed of their symptoms until quite late while others find it difficult to minimise the effects of risk factors that cause heart diseases. Machine Learning Algorithms have been quite efficacious in producing results with a high level of correctness thereby preventing the onset of heart diseases in many patients and reducing the impact in the ones that are already affected by such diseases. It has helped medical researchers and doctors all over the world in recognising patterns in the patients resulting in early detections of heart diseases.

Comparative Study of Various Machine Learning Algorithms for Prediction of Insomnia

2019 ◽  
pp. 234-257 ◽  
Author(s):  
Ravinder Ahuja ◽  
Vishal Vivek ◽  
Manika Chandna ◽  
Shivani Virmani ◽  
Alisha Banga
Keyword(s):  
Machine Learning ◽  
Heart Diseases ◽  
False Positive Rate ◽  
Learning Algorithms ◽  
True Positive Rate ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Mobility Problem ◽  
Positive Rate ◽  
F Measure

An early diagnosis of insomnia can prevent further medical aids such as anger issues, heart diseases, anxiety, depression, and hypertension. Fifteen machine learning algorithms have been applied and 14 leading factors have been taken into consideration for predicting insomnia. Seven performance parameters (accuracy, kappa, the true positive rate, false positive rate, precision, f-measure, and AUC) are used and for implementation. The authors have used python language. The support vector machine is giving higher performance out of all algorithms giving accuracy 91.6%, f-measure is 92.13, and kappa is 0.83. Further, SVM is applied on another dataset of 100 patients and giving accuracy 92%. In addition, an analysis of the variable importance of CART, C5.0, decision tree, random forest, adaptive boost, and XG boost is calculated. The analysis shows that insomnia primarily depends on the factors, which are the vision problem, mobility problem, and sleep disorder. This chapter mainly finds the usages and effectiveness of machine learning algorithms in Insomnia diseases prediction.

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