Breast cancer is the second leading cause of death for women, so accurate early detection can help decrease breast cancer mortality rates. Computer-aided detection allows radiologists to detect abnormalities efficiently. Medical images are sources of information relevant to the detection and diagnosis of various diseases and abnormalities. Several modalities allow radiologists to study the internal structure, and these modalities have been met with great interest in several types of research. In some medical fields, each of these modalities is of considerable significance. This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress in this area. This review reflects on the classification of breast cancer utilizing multi-modalities medical imaging. Details are also given on techniques developed to facilitate the classification of tumors, non-tumors, and dense masses in various medical imaging modalities. It first provides an overview of the different approaches to machine learning, then an overview of the different deep learning techniques and specific architectures for the detection and classification of breast cancer. We also provide a brief overview of the different image modalities to give a complete overview of the area. In the same context, this review was performed using a broad variety of research databases as a source of information for access to various field publications. Finally, this review summarizes the future trends and challenges in the classification and detection of breast cancer.

Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review

PurposeThe aim of this study was to assess the potential of machine learning with multiparametric magnetic resonance imaging (mpMRI) for the early prediction of pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and of survival outcomes in breast cancer patients.Materials and Met

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Impact of Machine Learning With Multiparametric Magnetic Resonance Imaging of the Breast for Early Prediction of Response to Neoadjuvant Chemotherapy and Survival Outcomes in Breast Cancer Patients.

BACKGROUND:
Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the early diagnosis of this cancer. The proposed model is the combination of rules and different machine learning techniques. Machine learning models can help physicians to reduce the number of false decisions. They try to exploit patterns and relationships among a large number of cases and predict the outcome of a disease using historical cases stored in datasets.
OBJECTIVE:
The objective of this study is to propose a rule-based classification method with machine learning techniques for the prediction of different types of Breast cancer survival.
METHODS:
We use a dataset with eight attributes that include the records of 900 patients in which 876 patients (97.3%) and 24 (2.7%) patients were females and males respectively. Naive Bayes (NB), Trees Random Forest (TRF), 1-Nearest Neighbor (1NN), AdaBoost (AD), Support Vector Machine (SVM), RBF Network (RBFN), and Multilayer Perceptron (MLP) machine learning techniques with 10-cross fold technique were used with the proposed model for the prediction of breast cancer survival. The performance of machine learning techniques were evaluated with accuracy, precision, sensitivity, specificity, and area under ROC curve.
RESULTS:
Out of 900 patients, 803 patients and 97 patients were alive and dead, respectively. In this study, Trees Random Forest (TRF) technique showed better results in comparison to other techniques (NB, 1NN, AD, SVM and RBFN, MLP). The accuracy, sensitivity and the area under ROC curve of TRF are 96%, 96%, 93%, respectively. However, 1NN machine learning technique provided poor performance (accuracy 91%, sensitivity 91% and area under ROC curve 78%).
CONCLUSIONS:
This study demonstrates that Trees Random Forest model (TRF) which is a rule-based classification model was the best model with the highest level of accuracy. Therefore, this model is recommended as a useful tool for breast cancer survival prediction as well as medical decision making.

Machine learning models in breast cancer survival prediction

Diabetes is a dreadful disease identified by escalated levels of glucose in the blood Machine learning algorithms help in identification and prediction of diabetes at an early stage The main objective of this study is to predict diabetes mellitus with better accuracy using an ensemble of machine learning algorithms The Pima Indians Diabetes dataset has been considered for experimentation, which gathers details of patients with and without having diabetes The proposed ensemble soft voting classifier gives binary classification and uses the ensemble of three machine learning algorithms viz random forest, logistic regression, and Naive Bayes for the classification Empirical evaluation of the proposed methodology has been conducted with state-of-the-art methodologies and base classifiers such as AdaBoost, Logistic Regression,Support Vector machine, Random forest, Naive Bayes, Bagging, GradientBoost, XGBoost, CatBoost by taking accuracy, precision, recall, F1-score as the evaluation criteria The proposed ensemble approach gives the highest accuracy, precision, recall, and F1_score value with 7904%, 7348%, 7145% and 806% respectively on the PIMA diabetes dataset Further, the efficiency of the proposed methodology has also been compared and analysed with breast cancer dataset The proposed ensemble soft voting classifier has given 9702% accuracy on the breast cancer dataset

An ensemble approach for classification and prediction of diabetes mellitus using soft voting classifier

Most screening tests for T2DM in use today were developed using multivariate regression methods that are often further simplified to allow transformation into a scoring formula. The increasing volume of electronically collected data opened the opportunity to develop more complex, accurate prediction models that can be continuously updated using machine learning approaches. This study compares machine learning-based prediction models (i.e. Glmnet, RF, XGBoost, LightGBM) to commonly used regression models for prediction of undiagnosed T2DM. The performance in prediction of fasting plasma glucose level was measured using 100 bootstrap iterations in different subsets of data simulating new incoming data in 6-month batches. With 6 months of data available, simple regression model performed with the lowest average RMSE of 0.838, followed by RF (0.842), LightGBM (0.846), Glmnet (0.859) and XGBoost (0.881). When more data were added, Glmnet improved with the highest rate (+ 3.4%). The highest level of variable selection stability over time was observed with LightGBM models. Our results show no clinically relevant improvement when more sophisticated prediction models were used. Since higher stability of selected variables over time contributes to simpler interpretation of the models, interpretability and model calibration should also be considered in development of clinical prediction models.

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Early detection of type 2 diabetes mellitus using machine learning-based prediction models.

Background: Accurate and timely diagnosis and effective prognosis of the disease is important to provide the best possible care for patients with COVID-19 and reduce the burden on the health care system. Machine learning methods can play a vital role in the diagnosis of COVID-19 by processing chest x-ray images.
Objective: The aim of this study is to summarize information on the use of intelligent models for the diagnosis and prognosis of COVID-19 to help with early and timely diagnosis, minimize prolonged diagnosis, and improve overall health care.
Methods: A systematic search of databases, including PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv, was performed for COVID-19–related studies published up to May 24, 2020. This study was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. All original research articles describing the application of image processing for the prediction and diagnosis of COVID-19 were considered in the analysis. Two reviewers independently assessed the published papers to determine eligibility for inclusion in the analysis. Risk of bias was evaluated using the Prediction Model Risk of Bias Assessment Tool.
Results: Of the 629 articles retrieved, 44 articles were included. We identified 4 prognosis models for calculating prediction of disease severity and estimation of confinement time for individual patients, and 40 diagnostic models for detecting COVID-19 from normal or other pneumonias. Most included studies used deep learning methods based on convolutional neural networks, which have been widely used as a classification algorithm. The most frequently reported predictors of prognosis in patients with COVID-19 included age, computed tomography data, gender, comorbidities, symptoms, and laboratory findings. Deep convolutional neural networks obtained better results compared with non–neural network–based methods. Moreover, all of the models were found to be at high risk of bias due to the lack of information about the study population, intended groups, and inappropriate reporting.
Conclusions: Machine learning models used for the diagnosis and prognosis of COVID-19 showed excellent discriminative performance. However, these models were at high risk of bias, because of various reasons such as inadequate information about study participants, randomization process, and the lack of external validation, which may have resulted in the optimistic reporting of these models. Hence, our findings do not recommend any of the current models to be used in practice for the diagnosis and prognosis of COVID-19.

Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review.

Left ventricular (LV) apical aneurysms in hypertrophic cardiomyopathy (HCM) are a recognized risk marker for adverse cardiovascular events. There is variable practice among clinicians and discordance between international guidelines regarding treatment recommendations and prognostication for this important phenotype.The authors sought to describe the morphology, clinical course, and risk of adverse events in a large single-center cohort of HCM patients with LV apical aneurysms.This study analyzed 160 HCM patients with an LV apical aneurysm who were evaluated in our dedicated HCM clinic between January 1997 and April 2021.Mean age was 59.1 ± 13.6 years, and 71% of these patients were male. Mean aneurysm size was 1.77 ± 1.04 cm. Over 6.2 ± 4.8 years, 14 (9%) patients had a sudden cardiac death (SCD) event, including appropriate therapy from an implantable cardioverter-defibrillator (ICD) or resuscitation from cardiac arrest (annualized event rate 1.77%/y), 39 (24%) had either a thromboembolic stroke or apical thrombus formation (2.9%/y), and 14 (9%) developed LV systolic dysfunction with an ejection fraction (EF) <50% (1.28%/y). HRs for SCD, stroke or thrombus, and EF <50% per 1-cm increase in aneurysm size were 1.69 (P = 0.007), 1.60 (P = 0.0002), and 1.63 (P = 0.01), respectively. Aneurysm size ≥2 cm was associated with a 5-year SCD rate of 9.7%, compared with 2.9% for aneurysm size <2 cm (log-rank P = 0.037). This subgroup also had higher risk of stroke/thrombus formation (HR: 2.20; P = 0.002), with an annualized event rate of 2.7%/year. A total of 39 (24%) patients reached the combined end point of SCD, stroke, or LV dysfunction (2.12%/y) with an HR of 1.47/cm increase in aneurysm size (P = 0.003) and an HR of 2.22 for patients with aneurysm size ≥2 cm (P = 0.02).Increasing aneurysm size confers poorer prognosis. Aneurysm size ≥2 cm should alert potential consideration for prophylactic anticoagulation and primary prevention ICDs. 

Clinical Characteristics and Prognostic Importance of Left Ventricular Apical Aneurysms in Hypertrophic Cardiomyopathy.

The most common gender-specific malignancies are cancers of the breast and the prostate. In developing countries, cancer screening of all at risk is impractical because of healthcare resource limitations. Thus, determining high-risk areas might be an important first screening step. This study explores incidence patterns of potential high-risk clusters of breast and prostate cancers in southern Iran. This cross-sectional study was conducted in the province of Kerman, South Iran. Patient data were aggregated at the county and district levels calculating the incidence rate per 100,000 people both for cancers of the breast and the prostate. We used the natural-break classification with five classes to produce descriptive maps. A spatial clustering analysis (Anselin Local Moran’s I) was used to identify potential clusters and outliers in the pattern of these cancers from 2014 to 2017. There were 1350 breast cancer patients (including, 42 male cases) and 478 prostate cancer patients in the province of Kerman, Iran during the study period. After 45 years of age, the number of men with diagnosed prostate cancer increased similarly to that of breast cancer for women after 25 years of age. The age-standardised incidence rate of breast cancer for women showed an increase from 29.93 to 32.27 cases per 100,000 people and that of prostate cancer from 13.93 to 15.47 cases per 100,000 during 2014–2017. Cluster analysis at the county level identified high-high clusters of breast cancer in the north-western part of the province for all years studied, but the analysis at the district level showed high-high clusters for only two of the years. With regard to prostate cancer, cluster analysis at the county and district levels identified high-high clusters in this area of the province for two of the study years. North-western Kerman had a significantly higher incidence rate of both breast and prostate cancer than the average, which should help in designing tailored screening and surveillance systems. Furthermore, this study generates new hypotheses regarding the potential relationship between increased incidence of cancers in certain geographical areas and environmental risk factors.

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Spatio-temporal mapping of breast and prostate cancers in South Iran from 2014 to 2017.

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Mahdieh Montazeri

Papers

Machine learning models in breast cancer survival prediction

Machine Learning Models for Image-Based Diagnosis and Prognosis of COVID-19: Systematic Review.

Clinical Characteristics and Prognostic Importance of Left Ventricular Apical Aneurysms in Hypertrophic Cardiomyopathy.

Spatio-temporal mapping of breast and prostate cancers in South Iran from 2014 to 2017.

Machine learning models for predicting the diagnosis of liver disease