A Novel Approach for Prediction of Heart Disease:  Machine Learning Techniques

Srinivas, V. Sesha; Aditya, K; G, Prasanth; Babukarthik, R. G.; Satheeshkumar, S.; Sambasivam, G.

doi:10.14419/ijet.v7i2.32.15380

Cited by 8 publications

(5 citation statements)

References 1 publication

(1 reference statement)

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“…The ability of machine learning models to automatically learn features makes image classification applicable to a wide range of domains [34]. From facial recognition and object detection to medical imaging and satellite image analysis, machine learning-powered image classification systems can handle complex visual data and provide valuable insights [35].…”

Section: Machine Learning Helps With Image Classificationmentioning

confidence: 99%

Blended Learning for Machine Learning-based Image Classification

2023

EAI Endorsed Trans e-Learn

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The paper commences with an introduction to blended learning, an educational approach that amalgamates traditional face-to-face instruction with online learning, aiming to capitalize on the advantages of conventional classroom instruction and digital resources in order to enhance the overall learning experience. The incorporation of diverse technologies facilitates a personalized learning experience that caters to the needs and learning styles of individual students. Image classification entails training machine learning models to categorize or label images into predetermined classes or categories, empowering machines to recognize and comprehend crucial components of visual information, emulating humans' classification of objects in the real world. The crux of image classification relies on extracting meaningful features from images and distinguishing different categories by associating specific features with distinct classes through iterative optimization learning. Machine learning significantly aids image classification by endowing automated systems with the capability to discern patterns, features, and distinctions within datasets, ultimately achieving accurate image classification. The integration of hybrid learning methods can augment the training process for machine learning models used in image classification by providing a flexible and adaptive learning environment.

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Section: Machine Learning Helps With Image Classificationmentioning

confidence: 99%

Blended Learning for Machine Learning-based Image Classification

2023

EAI Endorsed Trans e-Learn

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“…Praveena, M., Asha Deepika, R., and Sai Raghavendhar researched the prediction of heart disease using data mining approaches (Alotaibi, 2019). Sai Krishnasree, K., and M. R. Narasinga Rao conducted comparison research on Bayesian regularisation with multiple regression models (Pahwa) Siva Kumar, P., Anand, D., Uday Kumar, V., and Bhattacharyya conducted a study on a computational knowledge technique for the feasible detection of cardiac disease using a genetic algorithm (Srinivas, (2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Heart Disease Detection Using Machine Learning Techniques

Younas¹,

Ubaid²,

Raza³

et al. 2022

Int. J. Res. Publ. Rev.

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According to a recent World Health Organization survey, 17.9 million people die each year as a result of heart-related disorders, and the number is continuously growing. With a rising population and sickness, it is becoming more difficult to diagnose disease and provide pr oper therapy at the appropriate time. However, there is a ray of optimism in that recent technological breakthroughs have expedited the public health sector by generating sophisticated functional biomedical solutions.The purpose of this paper is to examine the different data mining techniques, namely Naive Bayes, Random Forest Classification, Decision Tree, and Support Vector Machine, using a qualified dataset for Heart disease prediction, which includes attributes such as gender, age, chest pain type, blood pressure, blood sugar, and so on. The research entails determining the correlations between the different qualities of the dataset using typical data mining techniques and then using the attributes to forecast the likelihood of a cardiac condition. These machine learning algorithms need less time to forecast disease with more precision, reducing the loss of priceless lives all across the world.

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“…This helps companies stop problems before they happen. It's like having a special tool to predict cyber attacks, so companies can be prepared and keep their business and reputation safe Srinivas [39] . AI can help cyber security teams by doing some of their work for them, like finding and fixing problems.…”

Section: Introduction 1overview Of Ai DL and Machine Learning In Cybe...mentioning

confidence: 99%

A Systematic Literature Review: AI, DL and Machine Learning inCyber Risk Management

Hamid,

Rahman

2024

Preprint

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The integration of AI and ML in both climate change and cyber risk management has significantly bolstered their effectiveness. These advanced technologies enable organizations to handle extensive datasets, whether related to climate patterns or cyber activities, with greater efficiency and making more informed decisions. AI and machine learning are utilized in cyber security to predict potential threats, automate tasks, and manage cyber security risks. Ensuring the accuracy of information, comprehending the functionality of the model, and safeguarding individuals' data pose challenges. It is very important to use personal information ethically and protect it. Researchers should prioritize accuracy in the information utilized, clarity in AI models, and fairness in AI decision-making in the future. New technologies like edge computing, federated learning, explainable AI, and quantum computing are changing the way we manage risk and keep our digital information safe. These new advancements provide opportunities to gain a better understanding of risks, identify threats more rapidly, and improve decision-making. Still, there are challenges concerning data accuracy, understanding model operations, and privacy issues that must be resolved before these technologies can effectively address cyber risks.

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A Novel Approach for Prediction of Heart Disease: Machine Learning Techniques

Cited by 8 publications

References 1 publication

Blended Learning for Machine Learning-based Image Classification

Blended Learning for Machine Learning-based Image Classification

Heart Disease Detection Using Machine Learning Techniques

A Systematic Literature Review: AI, DL and Machine Learning inCyber Risk Management

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