A Comparison of Neural Networks and Machine Learning Methods for Prediction of Heart Disease

Ghongade, Omkar Subhash; Reddy, S Kiran Sai; Tokala, Srilatha; Hajarathaiah, Koduru; Enduri, Murali Krishna; Anamalamudi, Satish

doi:10.1109/icct56969.2023.10076174

Cited by 8 publications

(2 citation statements)

References 15 publications

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“…Based on a variety of performance metrics, we have assessed the model's output. Performance metrics are crucial measurements for assessing how well a machine learning algorithm is working [24]. Performance metrics are used to compare how well several models perform on a given data set and to identify the model that yields the best results.…”

Section: Resultsmentioning

confidence: 99%

“…This approach equips our model with the capability to detect ALL regardless of the specific disease stage, enabling it to analyze and make predictions on images from any stage of the condition. In addition to this, we've conducted a comprehensive comparative analysis involving three prominent algorithms: Convolutional Neural Networks (CNN), VGG-16 Net [23], and Inception Net [24], to assess their performance in the context of ALL detection. This thorough evaluation provides valuable insights into the effectiveness of these pretrained neural networks in enhancing our model's predictive accuracy and overall robustness for this critical medical application.…”

Section: Selection and Optimization Trade-off Analysis 4 Performance ...mentioning

confidence: 99%

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Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning & Transfer Learning Technique

Ghongade,

Reddy,

Gavini

et al. 2023

IJEEI

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White blood cells called lymphocytes are the target of the blood malignancy known as acute lymphoblastic leukemia (ALL). In the domain of medical image analysis, deep learning and transfer learning methods have recently showcased significant promise, particularly in tasks such as identifying and categorizing various types of cancer. Using microscopic pictures, we suggest a deep learning and transfer learning-based method in this research work for predicting ALL blood cells. We use a pre-trained convolutional neural network (CNN) model to extract pertinent features from the microscopic images of blood cells during the feature extraction step. To accurately categorize the blood cells into leukemia and non-leukemia classes, a classification model is built using a transfer learning technique employing the collected features. We use a publicly accessible collection of microscopic blood cell pictures, which contains samples from both leukemia and non-leukemia, to assess the suggested method. Our experimental findings show that the suggested method successfully predicts ALL blood cells with high accuracy. The method enhances early ALL detection and diagnosis, which may result in better patient treatment outcomes. Future research will concentrate on larger and more varied datasets and investigate the viability of integrating it into clinical processes for real-time ALL prediction.

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Section: Resultsmentioning

confidence: 99%

Section: Selection and Optimization Trade-off Analysis 4 Performance ...mentioning

confidence: 99%

Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning & Transfer Learning Technique

Ghongade,

Reddy,

Gavini

et al. 2023

IJEEI

View full text Add to dashboard Cite

show abstract

Predicting Chronic Kidney Disease Progression Using Classification and Ensemble Learning

Valli Mayil,

Ratha Jeyalakshmi,

Gupta

2024

Data Management, Analytics and Innovation

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Smart Healthcare Systems: A Survey of IoT and AI Technologies for Heart Disease Prediction

Sebastian,

2024

2024 International Conference on Advancements in Power, Communication and Intelligent Systems (APCI)

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A Comparison of Neural Networks and Machine Learning Methods for Prediction of Heart Disease

Cited by 8 publications

References 15 publications

Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning & Transfer Learning Technique

Acute Lymphoblastic Leukemia Blood Cells Prediction Using Deep Learning & Transfer Learning Technique

Predicting Chronic Kidney Disease Progression Using Classification and Ensemble Learning

Smart Healthcare Systems: A Survey of IoT and AI Technologies for Heart Disease Prediction

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