Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis

peer reviewedEach year number of deaths is increasing extremely because of breast cancer. It is the most frequent type of all cancers and the major cause of death in women worldwide. Any development for prediction and diagnosis of cancer disease is capital important for a healthy life. Consequently, high accuracy in cancer prediction is important to update the treatment aspect and the survivability standard of patients. Machine learning techniques can bring a large contribute on the process of prediction and early diagnosis of breast cancer, became a research hotspot and has been proved as a strong technique. In this study, we applied five machine learning algorithms: Support Vector Machine (SVM), Random Forest, Logistic Regression, Decision tree (C4.5) and K-Nearest Neighbours (KNN) on the Breast Cancer Wisconsin Diagnostic dataset, after obtaining the results, a performance evaluation and comparison is carried out between these different classifiers. The main objective of this research paper is to predict and diagnosis breast cancer, using machine-learning algorithms, and find out the most effective whit respect to confusion matrix, accuracy and precision. It is observed that Support vector Machine outperformed all other classifiers and achieved the highest accuracy (97.2%).All the work is done in the Anaconda environment based on python programming language and Scikit-learn library

Breast Cancer Prediction and Diagnosis through a New Approach based on Majority Voting Ensemble Classifier

peer reviewedResearchers have extensively used machine learning techniques and data mining methods to build prediction models and classify data in various domains such as aviation, computer science, education, finance, marketing and particularly in medical field where those methods are applied as support systems for diagnosis and analysis in order to make better decisions. On this subject, our research paper attempts to assess the performance of Individual and Ensemble machine learning techniques based on the effectiveness and the efficiently, in terms of accuracy, specificity, sensitivity and precision to choose the most effective. The main object of our research paper is to define the best and effective machine learning approach for the Breast Cancer diagnosis and prediction. To achieve our objective, we applied individual based level machine learning algorithms Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Decision tree (C4.5), Simple Logistic and well known ensembles methods like Majority Voting and Random Forest with 10 cross field technique on the Breast Cancer Diagnosis Dataset obtained from UCI Repository. The experimental results show that the Majority Voting Ensemble technique based on 3 top classifiers SVM, K-NN, Simple Logistic gives the highest accuracy 98.1% with the lowest error rate 0.01% and outperformed all other individual classifiers. This study demonstrates that our proposal approach based on Majority Voting Ensemble technique was the best classification machine learning model with the highest level of accuracy for breast cancer prediction and diagnosis. All experiments are effectuated within a simulation environment and realized in Weka data mining tool

Lattice Boltzmann Simulation of Convective Heat Transfer from Heated Blocks in a Horizontal Channel

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COMPUTATIONAL INVESTIGATION OF LAMINAR FLOW AND CONVECTIVE HEAT TRANSFER IN AN OBSTRUCTED CHANNEL USING LATTICE BOLTZMANN METHOD

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Double MRT thermal lattice Boltzmann method for simulating convective flows

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Study of coupled double diffusive convection–radiation in a tilted cavity via a hybrid multi-relaxation time-lattice Boltzmann-finite difference and discrete ordinate methods

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Comparative study between direct steam generation and molten salt solar tower plants in the climatic conditions of the eastern Moroccan region

International audienceThis study deals with a numerical investigation to assess and compare the thermal and economic performance of two solar tower power systems. It concerns the Molten Salt (MS) and Direct Steam Generation (DSG) technologies used as heat carrier and storage. For this purpose, a 50 MWe solar tower plant without thermal energy storage under the climatic conditions of the eastern Moroccan region is simulated with the System Advisor Model (SAM) software. The meteorological data has been collected via a high precision meteorological station located in Oujda city(34°40'53'' N 1°54'30.9'' W). The results are presented in terms of monthly energy production, annual energy output, and Levelized Electricity Cost (LEC). From these findings, it can be concluded that, for an amount annual Direct Normal Irradiance (DNI) of 1989.9 kWh/m2/yr, the molten salt plant has the highest annual energy production than the DSG (86.3 GWh for MS against 83.3 GWh for DSG) and the LEC of the Molten salt plant is 12.5 % lower than the DSG plant. ©2020. CBIORE-IJRED. All rights reserve