Machine Learning–Based Disease Diagnosis and Prediction for E‐Healthcare System

Kute, Shruti; Madhav, A. V. Shreyas; Kumari, Shabnam; Aswathy, S. U.

doi:10.1002/9781119792437.ch6

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…(4) Within every memeplex, the frog culture searches for a space in dissimilar directions and independently exchanges ideas. The frog with the worst fitness value is represented by X b , whereas the frog with the best fitness value is denoted by X w : (5) The frog with the global optimum fitness is recognized as X g : (6) The frog with the worst fitness value is determined as follows.…”

Section: Processes Involved In Feature Selection Techniquementioning

confidence: 99%

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Artificial Intelligence Enabled Decision Support System on E-Healthcare Environment

Karthikeyan¹,

Nithya²,

Alkhayyat³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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In today's digital era, e-healthcare systems exploit digital technologies and telecommunication devices such as mobile devices, computers and the internet to provide high-quality healthcare services. E-healthcare decision support systems have been developed to optimize the healthcare services and enhance a patient's health. These systems enable rapid access to the specialized healthcare services via reliable information, retrieved from the cases or the patient histories. This phenomenon reduces the time taken by the patients to physically visit the healthcare institutions. In the current research work, a new Shuffled Frog Leap Optimizer with Deep Learning-based Decision Support System (SFLODL-DSS) is designed for the diagnosis of the Cardiovascular Diseases (CVD). The aim of the proposed model is to identify and classify the cardiovascular diseases. The proposed SFLODL-DSS technique primarily incorporates the SFLO-based Feature Selection (SFLO-FS) approach for feature subset election. For the purpose of classification, the Autoencoder with Gated Recurrent Unit (AEGRU) model is exploited. Finally, the Bacterial Foraging Optimization (BFO) algorithm is employed to fine-tune the hyperparameters involved in the AEGRU method. To demonstrate the enhanced performance of the proposed SFLODL-DSS technique, a series of simulations was conducted. The simulation outcomes established the superiority of the proposed SFLODL-DSS technique as it achieved the highest accuracy of 98.36%. Thus, the proposed SFLODL-DSS technique can be exploited as a proficient tool in the future for the detection and classification of CVD.

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Section: Processes Involved In Feature Selection Techniquementioning

confidence: 99%

“…Cardiovascular Disease (CVD) is one of the critical health problems with a high mortality rate in both developing as well as developed countries [6]. Some typical symptoms of CVD include body weakness, swollen feet and shortness of breath.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Enabled Decision Support System on E-Healthcare Environment

Karthikeyan¹,

Nithya²,

Alkhayyat³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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“…The specifi c tasks such as visual recognition of objects, identifi cation, and separation, deep CNNs are at present the most appreciated by computer vision researchers. Unlike conventional NN which receive vector input, CNNs operate on volumes, which is one of its advantages over ANNs (23). The term "parameter sharing" refers to the practice of more than one neuron in a given entity map using the same weights.…”

Section: Related Workmentioning

confidence: 99%

Alzheimer’ disease prediction and classification using CT images through machine learning

REDDY¹,

KRISHNA²

2023

BLL

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Numerous surveys using different techniques have been conducted in recent years to accurately classify Alzheimer's disease (AD). This research emphasized the identifi cation of AD through neuroimaging data. However, it is important to identify symptoms as soon as possible when the disease-modifying medications function best during infection before a permanent cognitive impairment develops. The use of automated algorithms to detect the early symptom of AD to this information was very important. Machine Learning (ML) has been proposed for the evaluation of various image segmentation and database techniques. In addition, Visual Geometry Group (VGG)-16 & Improved Faster Recurrent Convolutional Neural Network (IFRCNN) method developed for the ImageNet database utilizing the mathematical model based on action recognition as a feature extractor for categorization work. Experiments are being conducted on the Alzheimer's Neuroimaging Initiative (ADNI) dataset, and the proposed system achieves the 98.32 % accuracy level (Tab. 6, Fig. 4, Ref. 34).

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“…The efficacy of AI is judged by its accuracy, sensitivity, and specificity, with these three critical elements emphasised in its typical assessments. 60 Following a review of the literature, clinical aspects overseeing deep learning networks and neural pathways, employing support vector machines, nearest neighbours, random forests, decision trees, logistic regression, naive Bayes, discriminant analysis, and convolutional neural networks, can produce more comprehensive results. 61 Algorithm-based performance analysis may be performed, taking into consideration aspects such as origin, sample size, and the amount of features in training and testing samples.…”

Section: Ai In Disease Diagnosismentioning

confidence: 99%

Transformative trends: A comprehensive review on role of artificial intelligence in healthcare and pharmaceutical research

Bhabad,

Lamkhade,

Koyate

et al. 2023

IJCAAP

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Artificial intelligence (AI) represents a specialized field within computer science, empowering machines to efficiently process and analyze intricate data. The surge in AI-focused research has notably accelerated, particularly in its growing role within healthcare services and research. This review delves into the manifold opportunities and challenges that AI presents in the realms of healthcare and pharmaceutical research. To compile relevant literature, a comprehensive search was conducted across databases such as PubMed, Science Direct, and Google Scholar. The article extensively explores the application of AI in diverse healthcare aspects such as disease diagnosis, digital therapy, personalized treatment, drug discovery, and the anticipation of epidemics or pandemics. The predominant AI technologies employed include deep learning and neural networks. The review emphasizes the pivotal role of advancing AI technologies in fostering rapid and cost-effective healthcare and pharmaceutical research. The anticipated outcome is an improved delivery of services to the general public, reflecting the transformative impact of AI on scientific endeavors.

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Machine Learning–Based Disease Diagnosis and Prediction for E‐Healthcare System

Cited by 6 publications

References 25 publications

Artificial Intelligence Enabled Decision Support System on E-Healthcare Environment

Artificial Intelligence Enabled Decision Support System on E-Healthcare Environment

Alzheimer’ disease prediction and classification using CT images through machine learning

Transformative trends: A comprehensive review on role of artificial intelligence in healthcare and pharmaceutical research

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