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Zhang B, Shi H, Wang H. Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach. J Multidiscip Healthc 2023; 16:1779-1791. [PMID: 37398894 PMCID: PMC10312208 DOI: 10.2147/jmdh.s410301] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 06/12/2023] [Indexed: 07/04/2023] Open
Abstract
Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.
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Affiliation(s)
- Bo Zhang
- Jinling Institute of Science and Technology, Nanjing City, Jiangsu Province, People’s Republic of China
| | - Huiping Shi
- Jinling Institute of Science and Technology, Nanjing City, Jiangsu Province, People’s Republic of China
| | - Hongtao Wang
- School of Life Science, Tonghua Normal University, Tonghua City, Jilin Province, People’s Republic of China
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Diaz-del-Pino S, Trelles-Martinez R, González-Fernández F, Guil N. Artificial intelligence to assist specialists in the detection of haematological diseases. Heliyon 2023; 9:e15940. [PMID: 37215889 PMCID: PMC10195887 DOI: 10.1016/j.heliyon.2023.e15940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2023] [Revised: 04/27/2023] [Accepted: 04/28/2023] [Indexed: 05/24/2023] Open
Abstract
Artificial intelligence, particularly the growth of neural network research and development, has become an invaluable tool for data analysis, offering unrivalled solutions for image generation, natural language processing, and personalised suggestions. In the meantime, biomedicine has been presented as one of the pressing challenges of the 21st century. The inversion of the age pyramid, the increase in longevity, and the negative environment due to pollution and bad habits of the population have led to a necessity of research in the methodologies that can help to mitigate and fight against these changes. The combination of both fields has already achieved remarkable results in drug discovery, cancer prediction or gene activation. However, challenges such as data labelling, architecture improvements, interpretability of the models and translational implementation of the proposals still remain. In haematology, conventional protocols follow a stepwise approach that includes several tests and doctor-patient interactions to make a diagnosis. This procedure results in significant costs and workload for hospitals. In this paper, we present an artificial intelligence model based on neural networks to support practitioners in the identification of different haematological diseases using only rutinary and inexpensive blood count tests. In particular, we present both binary and multiclass classification of haematological diseases using a specialised neural network architecture where data is studied and combined along it, taking into account the clinical knowledge of the problem, obtaining results up to 96% accuracy for the binary classification experiment. Furthermore, we compare this method against traditional machine learning algorithms such as gradient boosting decision trees and transformers for tabular data. The use of these machine learning techniques could reduce the cost and decision time and improve the quality of life for both specialists and patients while producing more precise diagnoses.
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Affiliation(s)
| | | | | | - Nicolas Guil
- Computer Architecture Department, University of Malaga, Spain
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3
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Lin W, Hu S, Wu Z, Xu Z, Zhong Y, Lv Z, Qiu W, Xiao X. iCancer-Pred: A tool for identifying cancer and its type using DNA methylation. Genomics 2022; 114:110486. [PMID: 36126833 DOI: 10.1016/j.ygeno.2022.110486] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 09/11/2022] [Accepted: 09/16/2022] [Indexed: 01/14/2023]
Abstract
DNA methylation is an important epigenetics, which occurs in the early stages of tumor formation. And it also is of great significance to find the relationship between DNA methylation and cancer. This paper proposes a novel model, iCancer-Pred, to identify cancer and classify its types further. The datasets of DNA methylation information of 7 cancer types have been collected from The Cancer Genome Atlas (TCGA). The coefficient of variation firstly is used to reduce the number of features, and then the elastic network is applied to select important features. Finally, a fully connected neural network is constructed with these selected features. In predicting seven types of cancers, iCancer-Pred has achieved an overall accuracy of over 97% accuracy with 5-fold cross-validation. For the convenience of the application, a user-friendly web server: http://bioinfo.jcu.edu.cn/cancer or http://121.36.221.79/cancer/ is available. And the source codes are freely available for download at https://github.com/Huerhu/iCancer-Pred.
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Affiliation(s)
- Weizhong Lin
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China.
| | - Siqin Hu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Zhicheng Wu
- Wuhan Ammunition Life Science & Technology Co., Ltd., Wuhan 430000, China
| | - Zhaochun Xu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Yu Zhong
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Zhe Lv
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Wangren Qiu
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
| | - Xuan Xiao
- School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen 333000, China
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Alsafi H, Munilla J, Rahebi J. An Approach for Cardiac Coronary Detection of Heart Signal Based on Harris Hawks Optimization and Multichannel Deep Convolutional Learning. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7276028. [PMID: 35942461 PMCID: PMC9356836 DOI: 10.1155/2022/7276028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 06/02/2022] [Indexed: 11/17/2022]
Abstract
Automatic diagnosis of arrhythmia by electrocardiogram has a significant role to play in preventing and detecting cardiovascular disease at an early stage. In this study, a deep neural network model based on Harris hawks optimization is presented to arrive at a temporal and spatial fusion of information from ECG signals. Compared with the initial model of the multichannel deep neural network mechanism, the proposed model of this research has a flexible input length; the number of parameters is halved and it has a more than 50% reduction in computations in real-time processing. The results of the simulation demonstrate that the approach proposed in this research had a rate of 96.04%, 93.94%, and 95.00% for sensitivity, specificity, and accuracy. Furthermore, the proposed approach has a practical advantage over other similar previous methods.
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Affiliation(s)
- Haedar Alsafi
- Department of Telecommunication Engineering, Malaga University, Malaga, Spain
| | - Jorge Munilla
- Department of Telecommunication Engineering, Malaga University, Malaga, Spain
| | - Javad Rahebi
- Department of Software Engineering, Istanbul Topkapi University, Istanbul, Turkey
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Ornaghi HL, Neves RM, Monticeli FM, Agnol LD. Dynamic mechanical and thermogravimetric properties of synthetized polyurethanes. Polym Bull (Berl) 2022. [DOI: 10.1007/s00289-022-04257-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Feature Importance Score-Based Functional Link Artificial Neural Networks for Breast Cancer Classification. BIOMED RESEARCH INTERNATIONAL 2022; 2022:2696916. [PMID: 35411308 PMCID: PMC8994690 DOI: 10.1155/2022/2696916] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 03/04/2022] [Accepted: 03/08/2022] [Indexed: 12/31/2022]
Abstract
Growth of malignant tumors in the breast results in breast cancer. It is a cause of death of many women across the world. As a part of treatment, a woman might have to go through painful surgery and chemotherapy that may further lead to severe side effects. However, it is possible to cure it if it is diagnosed in the initial stage. Recently, many researchers have leveraged machine learning (ML) techniques to classify breast cancer. However, these methods are computationally expensive and prone to the overfitting problem. A simple single-layer neural network, i.e., functional link artificial neural network (FLANN), is proposed to overcome this problem. Further, the F-score is used to reduce the issue of overfitting by selecting features having a higher significance level. In this paper, FLANN is proposed to classify breast cancer using Wisconsin Breast Cancer Dataset (WBCD) (with 699 samples) and Wisconsin Diagnostic Breast Cancer (WDBC) (with 569 samples) datasets. Experimental results reveal that the proposed models can diagnose breast cancer with higher performance. The proposed model can be used in the early breast cancer diagnosis with 99.41% accuracy.
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Performance Analysis of Deep Learning Models for Binary Classification of Cancer Gene Expression Data. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:1122536. [PMID: 35310177 PMCID: PMC8926523 DOI: 10.1155/2022/1122536] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 02/09/2022] [Indexed: 11/23/2022]
Abstract
The classification of patients as cancer and normal patients by applying the computational methods on their gene expression profiles is an extremely important task. Recently, deep learning models, mainly multilayer perceptron and convolutional neural networks, have gained popularity for being applied on this type of datasets. This paper aims to analyze the performance of deep learning models on different types of cancer gene expression datasets as no such consolidated work is available. For this purpose, three deep learning models along with two feature selection method and four cancer gene expression datasets have been considered. It has resulted in a total of 24 different combinations to be analyzed. Out of four datasets, two are imbalanced and two are balanced in terms of number of normal and cancer samples. Experimental results show that the deep learning models have performed well in terms of true positive rate, precision, F1-score, and accuracy.
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S V, A J, R S, Mohan S, Bhattacharya S, Kaluri R, Feng G, Tariq U. Multi-modal prediction of breast cancer using particle swarm optimization with non-dominating sorting. INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2020; 16:155014772097150. [DOI: 10.1177/1550147720971505] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
Cancer is enlisted as the second leading reason for death across the world wherein almost one person out of six dies of cancer. Breast cancer is one of the most common forms of cancer predominant in women having the second highest mortality rate in the world. Various scientific studies have been conducted to combat this disease, and machine learning approaches have been an extremely popular choice. Particle swarm optimization has been identified as one of the most powerful and efficient technique for the diagnosis of breast cancer guiding physicians towards timely and accurate treatment. It is also pertinent to mention that multi-modal prediction methods are used to make decisions depending upon different scenarios and aspects whereas the non-dominating sorting feature is useful to sort different objects based on differing requirements. The main novelty of this work is multi-modal prediction algorithm for breast cancer prediction is proposed. The work encompasses the use of particle swarm optimization, non-dominating sorting and multi-classifier techniques, namely, k-nearest neighbour method, fast decision tree and kernel density estimation. Finally, Bayes’ theorem is implemented for revising the results to achieve optimum accuracy in the breast cancer prediction. The proposed particle swarm optimization and non-domination sorting with classifier technique model helps to select the most significant features relevant to breast cancer predictions. The selected features design the objective of the problem model. The proposed model is implemented on the WBCD and WDBC breast cancer data sets publicly available from the UCI machine learning data repository. The metrics considered are sensitivity, specificity, accuracy and time complexity. The experimental results of the study using measures such as sensitivity, specificity, accuracy and time complexity. The experimental results of the study are evaluated against the state-of-the-art algorithms, namely, genetic algorithm kernel density estimation and particle swarm optimization kernel density estimation wherein the results justify the superiority of the proposed model.
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Affiliation(s)
- Vijayalakshmi S
- School of Computer Science and Engineering, Rajiv Gandhi College of Engineering & Technology, Puducherry, India
| | - John A
- School of Computer Science and Engineering, Galgotias University, Greater Noida, India
| | - Sunder R
- Department of Computer Science and Engineering, Sahrdaya College of Engineering and Technology, Thrissur, India
| | - Senthilkumar Mohan
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Sweta Bhattacharya
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Rajesh Kaluri
- School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, India
| | - Guang Feng
- Center of Network Information & Modern Education Technology, Guangdong University of Technology, Guangzhou, China
| | - Usman Tariq
- College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
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10
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Altameem T. Fuzzy rank correlation-based segmentation method and deep neural network for bone cancer identification. Neural Comput Appl 2020. [DOI: 10.1007/s00521-018-04005-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Park A, Nam S. Deep learning for stage prediction in neuroblastoma using gene expression data. Genomics Inform 2019; 17:e30. [PMID: 31610626 PMCID: PMC6808638 DOI: 10.5808/gi.2019.17.3.e30] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 09/10/2019] [Indexed: 12/13/2022] Open
Abstract
Neuroblastoma is a major cause of cancer death in early childhood, and its timely and correct diagnosis is critical. Gene expression datasets have recently been considered as a powerful tool for cancer diagnosis and subtype classification. However, no attempts have yet been made to apply deep learning using gene expression to neuroblastoma classification, although deep learning has been applied to cancer diagnosis using image data. Taking the International Neuroblastoma Staging System stages as multiple classes, we designed a deep neural network using the gene expression patterns and stages of neuroblastoma patients. Despite a small patient population (n = 280), stage 1 and 4 patients were well distinguished. If it is possible to replicate this approach in a larger population, deep learning could play an important role in neuroblastoma staging.
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Affiliation(s)
- Aron Park
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21565, Korea
| | - Seungyoon Nam
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon 21565, Korea.,Department of Genome Medicine and Science, College of Medicine, Gachon University, Incheon 21565, Korea.,Department of Life Sciences, Gachon University, Seongnam 13120, Korea.,Gachon Institute of Genome Medicine and Science, Gachon University Gil Medical Center, Incheon 21565, Korea
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12
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Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3824-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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13
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A survey towards an integration of big data analytics to big insights for value-creation. Inf Process Manag 2018. [DOI: 10.1016/j.ipm.2018.01.010] [Citation(s) in RCA: 183] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Collaborative representation-based classification of microarray gene expression data. PLoS One 2017; 12:e0189533. [PMID: 29236759 PMCID: PMC5728509 DOI: 10.1371/journal.pone.0189533] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Accepted: 11/27/2017] [Indexed: 11/19/2022] Open
Abstract
Microarray technology is important to simultaneously express multiple genes over a number of time points. Multiple classifier models, such as sparse representation (SR)-based method, have been developed to classify microarray gene expression data. These methods allocate the gene data points to different clusters. In this paper, we propose a novel collaborative representation (CR)-based classification with regularized least square to classify gene data. First, the CR codes a testing sample as a sparse linear combination of all training samples and then classifies the testing sample by evaluating which class leads to the minimum representation error. This CR-based classification approach is remarkably less complex than traditional classification methods but leads to very competitive classification results. In addition, compressive sensing approach is adopted to project the high-dimensional gene expression dataset to a lower-dimensional space which nearly contains the whole information. This compression without loss is beneficial to reduce the computational load. Experiments to detect subtypes of diseases, such as leukemia and autism spectrum disorders, are performed by analyzing the gene expression. The results show that the proposed CR-based algorithm exhibits significantly higher stability and accuracy than the traditional classifiers, such as support vector machine algorithm.
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Estimation of Vehicular Speed and Passenger Car Equivalent Under Mixed Traffic Condition Using Artificial Neural Network. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2017. [DOI: 10.1007/s13369-017-2597-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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