Automated disease diagnosis and precaution recommender system using supervised machine learning

Rustam, Furqan; Imtiaz, Zainab; Mehmood, Arif; Rupapara, Vaibhav; Choi, Gyu Sang; Din, Sadia; Ashraf, Imran

doi:10.1007/s11042-022-12897-x

Cited by 14 publications

(4 citation statements)

References 41 publications

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“…Machine learning applications in different domains such as image processing [30], computer vision [31,32], health care [33], edge computing [34], the Internet of things (IoT) [35], etc., helping to make this world fully automated and smart. This study used supervised machine learning models for the automatic detection of breast cancer using FNA features.…”

Section: Supervised Machine Learning Algorithmsmentioning

confidence: 99%

Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning

Shafique

Rustam

Choi

et al. 2023

Cancers

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Breast cancer is one of the most common invasive cancers in women and it continues to be a worldwide medical problem since the number of cases has significantly increased over the past decade. Breast cancer is the second leading cause of death from cancer in women. The early detection of breast cancer can save human life but the traditional approach for detecting breast cancer disease needs various laboratory tests involving medical experts. To reduce human error and speed up breast cancer detection, an automatic system is required that would perform the diagnosis accurately and timely. Despite the research efforts for automated systems for cancer detection, a wide gap exists between the desired and provided accuracy of current approaches. To overcome this issue, this research proposes an approach for breast cancer prediction by selecting the best fine needle aspiration features. To enhance the prediction accuracy, several feature selection techniques are applied to analyze their efficacy, such as principal component analysis, singular vector decomposition, and chi-square (Chi2). Extensive experiments are performed with different features and different set sizes of features to investigate the optimal feature set. Additionally, the influence of imbalanced and balanced data using the SMOTE approach is investigated. Six classifiers including random forest, support vector machine, gradient boosting machine, logistic regression, multilayer perceptron, and K-nearest neighbors (KNN) are tuned to achieve increased classification accuracy. Results indicate that KNN outperforms all other classifiers on the used dataset with 20 features using SVD and with the 15 most important features using a PCA with a 100% accuracy score.

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Section: Supervised Machine Learning Algorithmsmentioning

confidence: 99%

Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning

Shafique

Rustam

Choi

et al. 2023

Cancers

Self Cite

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“…The strategy consists of two modules: Using disease symptoms, safety measures, and the associated dataset, Module 1 trains machine learning models using SVM, random forest, and other methods. Users of Module-2 can speak symptoms into a microphone to have them converted to text by Google Speech Recognition ( Rustam et al, 2022 ). Deep learning can extract subtleties from huge datasets, so, the authors presented a neural network approach for trustworthy NSC fate forecasting.…”

Section: Related Workmentioning

confidence: 99%

Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers

Uppal,

Gupta,

Juneja

et al. 2023

Front. Bioeng. Biotechnol.

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The human brain is an extremely intricate and fascinating organ that is made up of the cerebrum, cerebellum, and brainstem and is protected by the skull. Brain stroke is recognized as a potentially fatal condition brought on by an unfavorable obstruction in the arteries supplying the brain. The severity of brain stroke may be reduced or controlled with its early prognosis to lessen the mortality rate and lead to good health. This paper proposed a technique to predict brain strokes with high accuracy. The model was constructed using data related to brain strokes. The aim of this work is to use Multi Layer Perceptron (MLP) as a classification technique for stroke data and used multi-optimizers that include Adaptive moment estimation with Maximum (AdaMax), Root Mean Squared Propagation (RMSProp) and Adaptive learning rate method (Adadelta). The experiment shows RMSProp optimizer is best with a data training accuracy of 95.8% and a value for data testing accuracy of 94.9%. The novelty of work is to incorporate multiple optimizers alongside the MLP classifier which offers a comprehensive approach to stroke prediction, providing a more robust and accurate solution. The obtained results underscore the effectiveness of the proposed methodology in enhancing the accuracy of brain stroke detection, thereby paving the way for potential advancements in medical diagnosis and treatment.

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“…In order to provide automated diagnosis and preventive measures for various diseases, Rustam et al [13] developed the Automated Disease Diagnostic and Precaution Recommender System. During the real-time evaluation, the suggested approach obtains a 99.9% accuracy rate.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Disease Prediction and Drug Recommendation Prototype by Using Multiple Approaches of Machine Learning Algorithms

Nayak,

Garanayak,

Swain

et al. 2023

IEEE Access

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Large blocks of data must be analyzed and explored by utilizing the data mining procedures in order to uncover significant patterns and trends. Medical databases are one area where the data mining procedures can be utilized. Many people all over the world are struggling with their health and medical diagnoses. Massive amounts of data are produced by hospital information systems (HIS), yet it might be difficult to extract knowledge from diagnosis case data. By just giving the symptoms they are experiencing, patients can quickly learn about the sickness they are experiencing and the medication that can assist, treat it using the approaches utilized in this paper. In this paper, we give drug recommendations relied on ratings and conditions to customers. Four distinct prototypes are utilized to predict the diseases. The Vader tool and sentiment analysis relied on NLP are utilized to analyze the reviews. And finally, probabilistic and weighted average methodologies are utilized to recommend the medications. Each model and strategy utilized in this paper is described in detail. The experimental findings presented in this work can be utilized in future studies and for a variety of different medicinal applications.

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Automated disease diagnosis and precaution recommender system using supervised machine learning

Cited by 14 publications

References 41 publications

Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning

Breast Cancer Prediction Using Fine Needle Aspiration Features and Upsampling with Supervised Machine Learning

Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers

An Intelligent Disease Prediction and Drug Recommendation Prototype by Using Multiple Approaches of Machine Learning Algorithms

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