Effective Study of Machine Learning Algorithms for Heart Disease Prediction

Gaikwad, Mihir J.; Asole, Prathmesh S.; Bitla, Leela

doi:10.1109/parc52418.2022.9726613

Cited by 19 publications

(2 citation statements)

References 11 publications

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“…The block schematic of plant disease detection system shown in figure 1. Images from the input dataset are converted into the format needed by the machine learning algorithm [30] in the image pre-processing block before being passed on to the algorithm. The output of image preprocessing block applied to convolutional neural network model for feature extraction.…”

Section: Proposed Methodologymentioning

confidence: 99%

The Convolutional Neural Network for Plant Disease Detection Using Hierarchical Mixed Pooling Technique with Smoothing to Sharpening Approach

Kalbande¹,

Patil²

2023

IJCDS

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Plant health is an important factor in agricultural production as it mostly affected by plant diseases. Due to plant diseases, the growth and crop yield gets affected which results in negative impact on agriculture in terms of economic loss to farmers. In plant disease management, early and accurate disease detection can control its spreading and avoid unnecessary loss to farmers. Traditionally, plant disease detection has been carried out through visual inspection by human experts. This method is based on subjective perception hence it has risk for error in detecting accurate disease. In recent past, researchers have proposes numerous machine learning approaches to detect the plant diseases. Due to advancement in artificial intelligence and electronic gadgets technology, there is large scope for improvement in neural network algorithms for detecting plant diseases early and accurately by extracting leaves features efficiently. To detect tomato plant diseases, the novel convolutional neural network (CNN) model has been proposed in this paper. The hierarchical mixed pooling technique for smoothing to sharpening approach has been used in proposed CNN model. The system uses tomato plant leaf images obtained from Kaggle dataset. The system has been trained with 1000 images of healthy leaf and 1000 images each for nine different diseases frequently occurs in tomato plant. The different training models has been framed and experimented to identify efficient hierarchy of pooling techniques. The CNN training model 3 exhibit smoothing to sharpening approach with "Average-Max-GlobalMax" mixed pooling hierarchy and depicts better performance with a training loss 28.88%, a validation loss 12.61%, a training accuracy 96.46%, and a validation accuracy 95.41% at 20 epochs. Also, the performance of designed system have been evaluated with different state-of-art deep learning algorithms and compared with proposed CNN model.

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Section: Proposed Methodologymentioning

confidence: 99%

The Convolutional Neural Network for Plant Disease Detection Using Hierarchical Mixed Pooling Technique with Smoothing to Sharpening Approach

Kalbande¹,

Patil²

2023

IJCDS

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“…A machine learning approach to cardiovascular disease … (Abdikadir Hussein Elmi) 1031 frequently manifest without readily apparent symptoms underscores the critical need for early and accurate risk evaluation to facilitate prompt medical interventions [3]- [5].…”

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confidence: 99%

A machine learning approach to cardiovascular disease prediction with advanced feature selection

Abdullahi,

Ali Barre,

Hussein Elmi

2024

IJEECS

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<div align="center">Cardiovascular diseases (CVDs) pose a significant global public health challenge, necessitating precise risk assessment for proactive treatment and optimal utilization of healthcare resources. This study employs machine learning algorithms and sophisticated feature selection techniques to enhance the accuracy and comprehensibility of CVD prediction models. While traditional risk assessment tools are valuable, they frequently fail to consider the myriad intricate factors that contribute to the heightened risk of CVD. Our methodology employs machine learning algorithms to analyze diverse healthcare data sources and produce advanced predictive models. The salient feature of this research lies in the meticulous application of advanced feature selection techniques, enabling the identification of pivotal factors within heterogeneous datasets. Optimizing feature selection enhances the interpretability of the model, reduces dimensionality, and improves predictive accuracy. The area under the ROC curve (AUC-ROC) score of the wrapper method model significantly decreased from 95.1% to 75.1% after tuning, based on empirical tests that supported the suggested method. This showcases its capacity as a tool for assessing premature CVD susceptibility and developing tailored healthcare strategies. The study highlights the significance of integrating machine learning with feature selection due to the widespread influence of cardiovascular diseases. Integrating this system has the potential to enhance patient care and optimize the utilization of healthcare resources.</div>

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Optimizing Coronary Illness Prediction Using Hyperparameter Tuning Through Machine Learning

Vaishnavi,

Shanthi

2024

Communications in Computer and Information Science

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Effective Study of Machine Learning Algorithms for Heart Disease Prediction

Cited by 19 publications

References 11 publications

The Convolutional Neural Network for Plant Disease Detection Using Hierarchical Mixed Pooling Technique with Smoothing to Sharpening Approach

The Convolutional Neural Network for Plant Disease Detection Using Hierarchical Mixed Pooling Technique with Smoothing to Sharpening Approach

A machine learning approach to cardiovascular disease prediction with advanced feature selection

Optimizing Coronary Illness Prediction Using Hyperparameter Tuning Through Machine Learning

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