Development of machine learning model for diagnostic disease prediction based on laboratory tests

Park, Dong Jin; Park, Min Woo; Lee, Homin; Kim, Young‐Jin; Kim, Yeongsic; Park, Young Hoon

doi:10.1038/s41598-021-87171-5

Cited by 89 publications

(51 citation statements)

References 41 publications

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“…Three common indicators-Sensitivity, Specificity, and Precision-are defined below Accuracy [34] is defined as: F1 score considers both the precision and the sensitivity [35]. It is the harmonic mean of the previous two measures: precision and sensitivity.…”

Section: Measures and Indicatorsmentioning

confidence: 99%

Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM

et al. 2022

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Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis . Our study investigates the recognition of secondary pulmonary (SPTB). A novel F3 model is proposed. The first F means using a four-direction varying-distance gray-level co-occurrence matrix (FDVDGLCM) to analyze the chest CT images; the second F means a five-property feature set (FPFS) from the FDVDGLCM results; the third F means fuzzy support vector machine (FSVM). Besides, a slight adaption of multiple-way data augmentation is used to boost the training set. The 10 runs of 10-fold cross-validation demonstrate that this F3 model achieves a sensitivity of 93.68% ± 1.75%, a specificity of 94.17% ± 1.68%, a precision of 94.17% ± 1.55%, an accuracy of 93.92% ± 1.05%, an F1 score of 93.91% ± 1.07%, an MCC of 87.88% ± 2.09%, and an FMI of 93.92% ± 1.06%. The AUC is 0.9624. The FSVM can give better performance than ordinary SVM. The proposed F3 model is superior to six state-of-the-art SPTB recognition models.

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Section: Measures and Indicatorsmentioning

confidence: 99%

Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM

et al. 2022

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“…In this study, the author assessed the predictability of commonly used supervised algorithms to detect blood diseases, and they achieved ~98% accuracy to predict the occurrence of blood disease with LogitBoost algorithms [ 108 ]. Park et al built three models, LightGBM and extreme gradient boosting (XGBoost) ML models and a DNN (deep neural network) based model on 5145 cases and 326686 laboratory tests [ 109 ]. The authors proposed that among the three models, the ensemble model showed 81% F1-score and ~92% prediction accuracy against the most common diseases [ 109 ].…”

Section: Role Of Ai In the Screening Of Covid-19 Infected Patients And Diagnosismentioning

confidence: 99%

“…Park et al built three models, LightGBM and extreme gradient boosting (XGBoost) ML models and a DNN (deep neural network) based model on 5145 cases and 326686 laboratory tests [ 109 ]. The authors proposed that among the three models, the ensemble model showed 81% F1-score and ~92% prediction accuracy against the most common diseases [ 109 ]. Not only does this blood analysis detect the disease, but it can also tell about the severity of a disease.…”

Section: Role Of Ai In the Screening Of Covid-19 Infected Patients And Diagnosismentioning

confidence: 99%

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

et al. 2021

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As of August 6th, 2021, the World Health Organization has notified 200.8 million laboratory-confirmed infections and 4.26 million deaths from COVID-19, making it the worst pandemic since the 1918 flu. The main challenges in mitigating COVID-19 are effective vaccination, treatment, and agile containment strategies. In this review, we focus on the potential of Artificial Intelligence (AI) in COVID-19 surveillance, diagnosis, outcome prediction, drug discovery and vaccine development. With the help of big data, AI tries to mimic the cognitive capabilities of a human brain, such as problem-solving and learning abilities. Machine Learning(ML), a subset of AI, holds special promise for solving problems based on experiences gained from the curated data. Advances in AI methods have created an unprecedented opportunity for building agile surveillance systems using the deluge of real-time data generated within a short span of time. During the COVID-19 pandemic, many reports have discussed the utility of AI approaches in prioritization, delivery, surveillance, and supply chain of drugs, vaccines, and non-pharmaceutical interventions. This review will discuss the clinical utility of AI-based models and will also discuss limitations and challenges faced by AI systems, such as model generalizability, explainability, and trust as pillars for real-life deployment in healthcare.

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“…And, for performance enhancement, ensemble machine learning and deep learning model can be used [21,22]. In the healthcare domain, artificial intelligence (AI) plays a major role in automating the roles involved in disease diagnosis and treatment suggestions and also schedules perfect timing by the medical practitioners to perform various obligations that cannot be automated [23].…”

Section: Introductionmentioning

confidence: 99%

Identification and Prediction of Chronic Diseases Using Machine Learning Approach

Rayan

2022

Journal of Healthcare Engineering

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Nowadays, humans face various diseases due to the current environmental condition and their living habits. The identification and prediction of such diseases at their earlier stages are much important, so as to prevent the extremity of it. It is difficult for doctors to manually identify the diseases accurately most of the time. The goal of this paper is to identify and predict the patients with more common chronic illnesses. This could be achieved by using a cutting-edge machine learning technique to ensure that this categorization reliably identifies persons with chronic diseases. The prediction of diseases is also a challenging task. Hence, data mining plays a critical role in disease prediction. The proposed system offers a broad disease prognosis based on patient’s symptoms by using the machine learning algorithms such as convolutional neural network (CNN) for automatic feature extraction and disease prediction and K-nearest neighbor (KNN) for distance calculation to find the exact match in the data set and the final disease prediction outcome. A collection of disease symptoms has been performed for the preparation of the data set along with the person’s living habits, and details related to doctor consultations are taken into account in this general disease prediction. Finally, a comparative study of the proposed system with various algorithms such as Naïve Bayes, decision tree, and logistic regression has been demonstrated in this paper.

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Development of machine learning model for diagnostic disease prediction based on laboratory tests

Cited by 89 publications

References 41 publications

Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM

Secondary Pulmonary Tuberculosis Recognition by 4-Direction Varying-Distance GLCM and Fuzzy SVM

Artificial Intelligence in Surveillance, Diagnosis, Drug Discovery and Vaccine Development against COVID-19

Identification and Prediction of Chronic Diseases Using Machine Learning Approach

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