Suitable Supervised Machine Learning Techniques For Malignant Mesothelioma Diagnosis

Win, Khin Yadanar; Maneerat, Noppadol; Choomchuay, Somsak; Sreng, Syna; Hamamoto, Kazuhiko

doi:10.1109/bmeicon.2018.8609935

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…When they develop, metastasis from cancer in another part of the body is frequently the cause. The PCA–BEL categorised the information with a mean response rate of 100%, 96%, 98.32%, 87.40%, and 88%, as per the findings [ 16 ]. For the mesothelioma dataset, numerous “machine learning” methods are already in use.…”

Section: Discussionmentioning

confidence: 99%

[Retracted] Applications of Neural Network‐Based Plan‐Cancer Method for Primary Diagnosis of Mesothelioma Cancer

Kapila

Panwar

Raja

et al. 2023

BioMed Research International

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“Malignant mesothelioma (MM)” is an uncommon although fatal form of cancer. The proper MM diagnosis is crucial for efficient therapy and has significant medicolegal implications. Asbestos is a carcinogenic material that poses a health risk to humans. One of the most severe types of cancer induced by asbestos is “malignant mesothelioma.” Prolonged shortness of breath and continuous pain are the most typical symptoms of the condition. The importance of early treatment and diagnosis cannot be overstated. The combination “epithelial/mesenchymal appearance of MM,” however, makes a definite diagnosis difficult. This study is aimed at developing a deep learning system for medical diagnosis MM automatically. Otherwise, the sickness might cause patients to succumb to death in a short amount of time. Various forms of artificial intelligence algorithms for successful “Malignant Mesothelioma illness” identification are explored in this research. In relation to the concept of traditional machine learning, the techniques support “Vector Machine, Neural Network, and Decision Tree” are chosen. SPSS has been used to analyze the result regarding the applications of Neural Network helps to diagnose MM.

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Section: Discussionmentioning

confidence: 99%

[Retracted] Applications of Neural Network‐Based Plan‐Cancer Method for Primary Diagnosis of Mesothelioma Cancer

Kapila

Panwar

Raja

et al. 2023

BioMed Research International

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“…We shall train and test the performance of the following popular machine learning models: SVM, NB, LogR, and RF. Those classifiers have been chosen for their reported good performance scores as reported in [35][36] [37][38] [39]. As recommended by [40], we will also investigate whether an ensemble method may yield better performance than the selected machine learning algorithms alone.…”

Section: E Model Trainingmentioning

confidence: 99%

A Comparison of Machine Learning Models to Prioritise Emails using Emotion Analysis for Customer Service Excellence

Chuttur

Parianen

2022

Knowl. Eng. Data Sci.

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There has been little research on machine learning for email prioritization for customer service excellence. To fill this gap, we propose and assess the efficacy of various machine learning techniques for classifying emails into three degrees of priority: high, low, and neutral, based on the emotions inherent in the email content. It is predicted that after emails are classified into those three categories, recipients will be able to respond to emails more efficiently and provide better customer service. We use the NRC Emotion Lexicon to construct a labeled email dataset of 517,401 messages for our proposal. Following that, we train and test four prominent machine learning models, MNB, SVM, LogR, and RF, and an Ensemble of MNB, LSVC, and RF classifiers, on the labeled dataset. Our main findings suggest that machine learning may be used to classify emails based on their emotional content. However, some models outperform others. During the testing phase, we also discovered that the LogR and LSVC models performed the best, with an accuracy of 72%, while the MNB classifier performed the poorest. Furthermore, classification performance differed depending on whether the dataset was balanced or imbalanced. We conclude that machine learning models that employ emotions for email classification are a promising avenue that should be explored further.

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“…They showed that ensemble techniques can accurately classify mesothelioma disease. [18] showed the success of Support Vector Machines (SVM), Decision Trees (DT), Logistic Regression (LR) and Random Forest (RF) on the diagnosis of mesothelioma.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Based Early Diagnosis System for Mesothelioma Disease

Şentürk

Çekiç

2020

Düzce Üniversitesi Bilim Ve Teknoloji Dergisi

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Mesothelioma is pleura cancer that cause death in about one year after diagnosis. The disease causes pain and shortness of breath. Patients have a CT (Computed Tomography)-scan and lung x-ray traditionally, but the exact method is biopsy. There are also different biopsy methods for its diagnosis. Its prevalence is one or two in a million around the world, but for Turkey it is disastrous. Five hundred people are diagnosed as mesothelioma every year in Turkey. This serious rate makes early diagnosis systems crucial for mesothelioma. In this paper, a machine learning based early detection system has been proposed for this fatal disease. An open database is used for the experiments and different methods have been applied to the problem of diagnosing mesothelioma disease. Accuracy and sensitivity performance metrics were used for the evaluation of the methods. The results show the diagnostic performance of different machine learning methods and present a successful early diagnosis system.

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Suitable Supervised Machine Learning Techniques For Malignant Mesothelioma Diagnosis

Cited by 7 publications

References 13 publications

[Retracted] Applications of Neural Network‐Based Plan‐Cancer Method for Primary Diagnosis of Mesothelioma Cancer

[Retracted] Applications of Neural Network‐Based Plan‐Cancer Method for Primary Diagnosis of Mesothelioma Cancer

A Comparison of Machine Learning Models to Prioritise Emails using Emotion Analysis for Customer Service Excellence

A Machine Learning Based Early Diagnosis System for Mesothelioma Disease

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