Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

Kaur, Simarjeet; Singla, Jimmy; Nkenyereye, Lewis; Jha, Sudan; Prashar, Deepak; Joshi, Gyanendra Prasad; El–Sappagh, Shaker; Islam, Md. Saiful; Islam, S. M. Riazul

doi:10.1109/access.2020.3042273

Cited by 109 publications

(51 citation statements)

References 76 publications

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“…One of the most significant AI technologies in the medical field is a rule-based intelligent system, which provides a collection of if-then rules in medical healthcare and works as a decision support system. AI-based autonomous techniques that need very little human interaction are progressively displacing intelligent systems in the medical business [8].In recent years; medical aid software has been developed using computer technology and machine learning techniques as a support system for early identification of cardiovascular disease [9]. Early detection of any heart-related disease can lower the chance of mortality [10].…”

Section: Fig-1 Block Diagram Of the Medical Diagnosis Processmentioning

confidence: 99%

Comparative Study of Optimum Medical Diagnosis of Human Heart Disease Using Machine Learning Technique With and Without Sequential Feature Selection

et al. 2022

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Section: Fig-1 Block Diagram Of the Medical Diagnosis Processmentioning

confidence: 99%

Comparative Study of Optimum Medical Diagnosis of Human Heart Disease Using Machine Learning Technique With and Without Sequential Feature Selection

et al. 2022

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“…Several studies have highlighted implementation challenges encountered in precision medicine solutions [155] , [156] . These challenges include data preprocessing, unstructured clinical text processing, medical data processing and storage, and environmental data collections.…”

Section: Limitations In the Development Of Clinically Relevant Predictive Modelsmentioning

confidence: 99%

Machine learning in the prediction of cancer therapy

Rafique¹,

Islam

Kazi

2021

Computational and Structural Biotechnology Journal

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“…This section highlights the most recent machine learning approaches used to classify breast cancer based on the BDCW dataset. Furthermore, it displays different feature selection algorithms used to extract the most relevant features to enhance the ML approaches performance in classification, regression, and prediction processes [48][49][50][51].…”

Section: Related Workmentioning

confidence: 99%

Feature Selection Based Coral Reefs Optimization for Breast Cancer Classification

Abouelmagd¹,

Shams

El-Attar

et al. 2021

Studies in Computational Intelligence

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Early detection of cancer cases is one of the most important things that help complete treatment and disappearance of this disease from the human body. Breast cancer is the most widespread invasive cancer in women, and after lung cancer, it is the second leading cause of cancer death in women. The first symptoms of breast cancer usually appear as an area of thickened tissue in the breast or a lump in the breast or an armpit. Consequently, many features can be found to indicate the existence of cancer or not. This chapter employs the coral reefs optimization (CRO) algorithm for feature selection; the CRO has shown to be very effective with various classification approaches. In this chapter, we used five standard classifiers: Logistic Regression (LR), K-nearest neighbor (KNN), Support Vector Machine with Radial Basis Function (SVM-RBF), Random Forest (RF), Decision Tree (DT). All These classifiers are presented with and without feature selection using the CRO algorithm. The results indicated that using the feature selection based on CRO achieved better results before the feature selection. The most common dataset called Breast Diagnostic Cancer Wisconsin (BDCW) is utilized to select the most significant and classify the cancer cases with a tested accuracy of 100%, 99.1%, 100%, 100%, and 100% using LR, KNN, SVM-RBF, RF, and DT respectively.

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Medical Diagnostic Systems Using Artificial Intelligence (AI) Algorithms: Principles and Perspectives

Cited by 109 publications

References 76 publications

Comparative Study of Optimum Medical Diagnosis of Human Heart Disease Using Machine Learning Technique With and Without Sequential Feature Selection

Comparative Study of Optimum Medical Diagnosis of Human Heart Disease Using Machine Learning Technique With and Without Sequential Feature Selection

Machine learning in the prediction of cancer therapy

Feature Selection Based Coral Reefs Optimization for Breast Cancer Classification

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