An Innovative Machine Learning Approach to Diagnose Cancer at an Early Stage

Poongodi, P.; Udayakumar, E.; Srihari, K.; Sachi, Nandan Mohanty

doi:10.1002/9781119785620.ch13

Cited by 2 publications

(2 citation statements)

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“…Applying the information mentioned above to a set of machine learning methods confirmed our achieved findings. Utilizing machine learning methods in cancer diagnosis improves diagnostic accuracy and introduces novel and complex cause-and-effect relationships, which is not easily possible by examining and receiving a patient’s history [ 108 – 110 ]. Hirasawa et al used a neural network for detecting GC in endoscopic images.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls

et al. 2022

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Background Gastric cancer (GC) is the fifth most common cancer and the third cause of cancer deaths globally, with late diagnosis, low survival rate, and poor prognosis. This case-control study aimed to evaluate the expression of cystatin B (CSTB) and deleted in malignant brain tumor 1 (DMBT1) in the saliva of GC patients with healthy individuals to construct diagnostic algorithms using statistical analysis and machine learning methods. Methods Demographic data, clinical characteristics, and food intake habits of the case and control group were gathered through a standard checklist. Unstimulated whole saliva samples were taken from 31 healthy individuals and 31 GC patients. Through ELISA test and statistical analysis, the expression of salivary CSTB and DMBT1 proteins was evaluated. To construct diagnostic algorithms, we used the machine learning method. Results The mean salivary expression of CSTB in GC patients was significantly lower (115.55 ± 7.06, p = 0.001), and the mean salivary expression of DMBT1 in GC patients was significantly higher (171.88 ± 39.67, p = 0.002) than the control. Multiple linear regression analysis demonstrated that GC was significantly correlated with high levels of DMBT1 after controlling the effects of age of participants (R2 = 0.20, p < 0.001). Considering salivary CSTB greater than 119.06 ng/mL as an optimal cut-off value, the sensitivity and specificity of CSTB in the diagnosis of GC were 83.87 and 70.97%, respectively. The area under the ROC curve was calculated as 0.728. The optimal cut-off value of DMBT1 for differentiating GC patients from controls was greater than 146.33 ng/mL (sensitivity = 80.65% and specificity = 64.52%). The area under the ROC curve was up to 0.741. As a result of the machine learning method, the area under the receiver-operating characteristic curve for the diagnostic ability of CSTB, DMBT1, demographic data, clinical characteristics, and food intake habits was 0.95. The machine learning model’s sensitivity, specificity, and accuracy were 100, 70.8, and 80.5%, respectively. Conclusion Salivary levels of DMBT1 and CSTB may be accurate in diagnosing GCs. Machine learning analyses using salivary biomarkers, demographic, clinical, and nutrition habits data simultaneously could provide affordability models with acceptable accuracy for differentiation of GC by a cost-effective and non-invasive method.

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Section: Discussionmentioning

confidence: 99%

Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls

et al. 2022

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“…Cancer cells begin to grow because the skin cells' DNA is damaged from ultraviolet rays, and that causes mutations or triggers genetic mutations. This damage leads to multiplying of skin cells multiplying or growing at a much faster pace and causing malignant tumors also grow (Milton, 2018 ; Poongodi et al, 2021 ; Sharma et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Untangling Classification Methods for Melanoma Skin Cancer

Kumar

Vatsa²

2022

Front. Big Data

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Skin cancer is the most common cancer in the USA, and it is a leading cause of death worldwide. Every year, more than five million patients are newly diagnosed in the USA. The deadliest and most serious form of skin cancer is called melanoma. Skin cancer can affect anyone, regardless of skin color, race, gender, and age. The diagnosis of melanoma has been done by visual examination and manual techniques by skilled doctors. It is a time-consuming process and highly prone to error. The skin images captured by dermoscopy eliminate the surface reflection of skin and give a better visualization of deeper levels of the skin. However, the existence of many artifacts and noise such as hair, veins, and water residue make the lesion images very complex. Due to the complexity of images, the border detection, feature extraction, and classification process are challenging. Without a proper mechanism, it is hard to identify and predict melanoma at an early stage. Therefore, there is a need to provide precise details, identify early skin cancer, and classify skin cancer with appropriate sensitivity and precision. This article aims to review and analyze two deep neural network-based classification algorithms (convolutional neural network, CNN; recurrent neural network, RNN) and a decision tree-based algorithm (XG-Boost) on skin lesion images (ISIC dataset) and find which of these provides the best classification performance metric. Also, the performance of algorithms is compared using six different metrics—loss, accuracy, precision, recall, F1 score, and ROC.

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An Innovative Machine Learning Approach to Diagnose Cancer at an Early Stage

Cited by 2 publications

References 21 publications

Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls

Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls

Untangling Classification Methods for Melanoma Skin Cancer

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