Blockchain-Based Intelligent Agreement for Healthcare System: A Review

Raj, Anu; Prakash, Shiva; Srivastva, Jyoti; Gaur, Rajkumar

doi:10.1007/978-981-19-2821-5_53

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…The tools used to identify and diagnose such disorders, on the other hand, are complex and timeconsuming. Deep learning [5] algorithms that are often employed are essentially a subset of ANN (arti cial neural network), which have remarkable strength and variety in the learning process [6]. To prevent problems, a new but vital model is well-known for executing faultless acquiring attempts with substantial neural architecture evolution.…”

Section: Fungal Pneumoniamentioning

confidence: 99%

Identification and Classification of Pneumonia using CNN Model with Chest X- ray Image

Kumar

Prakash

2023

Preprint

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Pneumonia is a bacterial, fungal, or viral infection that affects one or both lungs. It is a serious disease in which the air sacs in both lungs become clogged with pus and other substances. So, several frameworks and models have been built to properly assess such diseases, but there is still space for improvement. In this study, we used CXR images to train a CNN model to detect and classify Pneumonia disease in the lungs and also present how training accuracy and validation accuracy as well as training loss and validation loss vary when changing the size of the input image. The Kaggle CXR dataset is used, which was already created and pre-processed. The Convolutional Neural Network method is used in the research in close collaboration with some other data augmentation frameworks to enhance classification accuracy, which also will help to enhance training and validation accuracies, as well as characterize the precision of the Convolutional Neural Network model and achieve various results. The training and validation accuracy of our model are 0.9757 and 0.9568, respectively, and the training and validation loss are 0.0857 and 0.1399.

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Section: Fungal Pneumoniamentioning

confidence: 99%

Identification and Classification of Pneumonia using CNN Model with Chest X- ray Image

Kumar

Prakash

2023

Preprint

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“…Throughout the testing period, actual records of patient are used to diagnose the occurrence of disease [6]. A medical expert is given access to patients' health records and the outcomes of the processing and will offer emergency assistance as needed [7][8]. The Heart Failure Clinical Records of UCI Repository dataset are utilized to determine the occurrence of heart disease in the general population [9].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning based Prediction and Diagnosis of Heart Disease using multiple models

Maurya

Prakash

2023

Preprint

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Nowadays, heart disease is considered to be the main cause of sickness. Since the majority of people are unaware of their own kind and severity of heart disease, heart disease is now a significant problem that affects people of all ages. On the other hand, manual approach of prediction is challenging and often requires the capability to choose the relevant approach. To resolve these issues, various machine-learning models are playing a vital role in automatic disease prediction in medical field. In this study, we have calculated and made a comparison of accuracy of various machine learning models such as SVM, KNN, Logistic Regression, Decision Tree, Random Forest, Gaussian Naive Bayes, AdaBoost, Extra Tree Classifier and Gradient Boosting for prediction of heart disease using UCI repository dataset for training and testing of models. Among all the models used, the highest accuracy of 95.08% obtained by the Gradient Boosting model The major aim of the paper is to get a reliable, computationally effective machine learning algorithm for heart disease prediction.

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Heart Disease Prediction and Diagnosis Using IoT, ML, and Cloud Computing

Maurya,

Prakash

2023

Lecture Notes in Networks and Systems

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Blockchain-Based Intelligent Agreement for Healthcare System: A Review

Cited by 5 publications

References 23 publications

Identification and Classification of Pneumonia using CNN Model with Chest X- ray Image

Identification and Classification of Pneumonia using CNN Model with Chest X- ray Image

Machine Learning based Prediction and Diagnosis of Heart Disease using multiple models

Heart Disease Prediction and Diagnosis Using IoT, ML, and Cloud Computing

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