Effective Decision Support Scheme Using Hybrid Supervised Machine Learning Procedure

Sharma, Yatendra Mohan; Saini, Pawan; Shalini, .; Sharma, Neelam

doi:10.1007/978-981-15-9689-6_61

Cited by 2 publications

(1 citation statement)

References 17 publications

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“…Since coronary disease is still the largest cause of death in nonindustrialized countries, it is critical to identify risk factors and early indications of the condition. Several published research [11][12][13][14][15][16][17] suggested that specialists in this field might be able to identify and prevent cardiac disease earlier than ever before through the appropriate application of machine learning techniques.. A strategy for predicting cardiovascular sickness was developed by researchers in [18] using machine learning techniques. The 303 cases and 17 characteristics of the Cleveland heart disease dataset from the UCI machine learning repository were used to create the data for the study.…”

Section: Related Workmentioning

confidence: 99%

A Predictive Study of Machine Learning and Deep Learning Procedures Over Chronic Disease Datasets

Seth

2024

JAIMLNN

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People's health and well-being are not given priority in the technological and Internet-savvy world we live in. People are becoming worse because they don't regularly attend the hospital for checkups due to job and unanticipated events. Most people nowadays suffer from one or more chronic illnesses, such as diabetes, hypothyroidism, heart disease, breast cancer, and dermatology. According to the World Health Organization (WHO), these chronic illnesses account for half of all fatalities in most nations and are the main cause of premature mortality. Patients who are identified early on potentially have their condition stop progressing. Many dispersed studies clearly demonstrated that conventional approaches to diagnosing chronic illnesses are prone to prejudice and heterogeneity among physicians, making it difficult to promptly and precisely diagnose problems. Still, Despite the availability of up-to-date information and a variety of machine learning-based methods, there have been enormous published efforts demonstrating that machine learning (ML)/deep learning (DL) based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. There are many machine learning-based techniques and current knowledge available, however despite this, a great deal of published research has shown that machine learning/deep learning based approach can considerably enhance the timely estimation of various health conditions. However, precise diagnosis of such diseases remains a difficulty. In order to tackle this problem, this work uses the UCI/KAGGLE ML/DL disease dataset to evaluate various ML/DL procedures and explores how different machine learning algorithms forecast chronic diseases. Accuracy and confusion matrix are used to verify the results. In order to help inexperienced researchers comprehend the disease prediction function of ML/DL-based techniques and determine the direction of Upcoming research, this study also discusses the advantages and disadvantages of accessible disease prediction schemes.

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