A Comprehensive Examination, Execution, and Evaluation of Machine Learning Algorithms Applied to a Breast Cancer Dataset Utilizing the WEKA Software Tool | Chapter 7 | Mathematics and Computer Science: Contemporary Developments Vol. 8

In the contemporary world, cancer ranks as one of the most critical diseases. The factors contributing to cancer include modern lifestyle choices, environmental influences, and genetic predispositions. It has emerged as the leading cause of mortality in developed nations. Among various types of cancer, Breast Cancer is the most prevalent among women and is a significant contributor to female mortality. Cancer screening methods are typically experimental or natural in their approach. Machine Learning algorithms play a crucial role in identifying significant patterns within data, particularly in the context of disease prediction. In this research, a new Machine Learning approach is developed in predicting breast cancer at an early stage. The objective is to assess the effectiveness of the ML algorithm based on metrics such as precision, specificity, sensitivity, and accuracy. A real-time Breast Cancer dataset was collected from the UCI Machine Learning repository for performing research. The data was firstly preprocessed to identify the missing values of the features in the dataset and replaced with the mean of the relevant variable. Then the dataset was projected on popular Machine Learning algorithms, including Sequential Minimal Optimization (SMO), Naive Bayes (NB), J48 (C4.5 decision tree), K-Means, and K-Nearest Neighbours (k-NN). From the analysis, it is observed that the SMO Machine Learning algorithm provides 96.90% accuracy which is very high when compared to the accuracy of existing approaches. The results indicate that SMO outperforms the others in terms of accuracy and low error rate.

 

Author (s) Details

 

K. SrikanthA
Department of Computer Science & Engineering, G. Pulla Reddy Engineering College (A), Kurnool, Andhra Pradesh, India.

 

S. Zahoor Ul Huq
Department of Computer Science & Engineering, G. Pulla Reddy Engineering College (A), Kurnool, Andhra Pradesh, India.

 

A.P. Siva Kumar
Department of Computer Science & Engineering, JNTUA, Anantapuramu, India.

 

Please see the book here:- https://doi.org/10.9734/bpi/mcscd/v8/2986 

Application of Data Mining Algorithms for the Detection of Non-functional Bore Wells | Chapter 1 | Research Developments in Science and Technology Vol. 9

India uses the most groundwater, with over 230 cubic kiloliters produced annually by its 27.5 million wells. Vertical artesian wells are dug in the surface's subsurface aquifer to gather water for a number of uses. of objectives. Due to the rising need for artesian wells, more are being drilled each year. factors including decreased rainfall, water scarcity, and groundwater depletion etc. The motor is removed as the water dries, and the exterior is not adequately sealed or covered. The borewell's diameter is big enough for a kid to fall into. into the interior of the at present disused or inoperative well. Apparently, one Sadly, more than 40 kids have fallen into wells since 2009, according to a report. portion of Rescue efforts have fallen short. The present study includes an analysis. of the Kaggle dataset derived from the drill holes. The purpose of this work is aims to create predictive models using basic machine learning techniques. ANN models, logistic regression, and bays to "forecasting existing equipment failure wells that will soon be reported and confiscated." i.e., to identify non-operational bore wells that demand quick intervention. The effectiveness of the measures were used to assess the model. Good results from logistic regression performance in identifying deadly inactive wells.

Author (s) Details

K. V. Uma
Department of Information Technology, Thiagarajar College of Engineering, Madurai, India.

T. Janani Priya
Department of Information Technology, Thiagarajar College of Engineering, Madurai, India.

K. P. Sabari Priya
Department of Information Technology, Thiagarajar College of Engineering, Madurai, India.

Jennifer Priscilla Vincent
Department of Information Technology, Thiagarajar College of Engineering, Madurai, India.

View Book:  https://stm.bookpi.org/RDST-V9/article/view/7546

A Comparative Study of Diagnosis of Lower Back Pain Based on Classification and Imaging Techniques | Chapter 14 | Current Topics on Mathematics and Computer Science Vol. 3

 Different classification approaches are compared in this research for the accurate diagnosis of Lower Back Pain using base and meta (Combination of Multiple Classifier for Training) level classifiers. Different imaging modalities based on radiology are also evaluated for diagnosing Lower Back Pain, such as Computed Tomography (CT) scans and Magnetic Resonance Imaging (MRI) (MRI). Lower back pain gets persistent as people age, so it's important to get a proper diagnosis early on. At the base and meta levels, five separate classifiers were implemented. Five distinct classifier combinations were developed at the meta level, using a voting mechanism. The overall classification utilising Nave Bayes and Multilayer Perceptron had the highest efficiency of 83.87 percent, according to the results. The goal of this study is to efficiently diagnose healthy people. to investigate the symptoms of lower back pain The dataset comes from Kaggle, a well-known predictive modelling site. The studies were conducted using the WEKA (Waikato Environment for Knowledge Analysis) software suite [1].

Author (S) Details

Dr. Mittal Desai
MCA Department, CMPICA, CHARUSAT University, Changa, India.

View Book :- https://stm.bookpi.org/CTMCS-V3/article/view/2054

Evaluation of Data Science Algorithm Using Prediction System: Government Schemes in Rural Sectors | Chapter 3 | Advanced Aspects of Engineering Research Vol. 12

 Each dataset is then imported into the Eunectes murinus Navigator at that moment. The examination and aggregation of the understudies' networks (SC, ST, BC, and MBC) and thus the plans are then carried out. Then, using forecasting algorithms (Nave Bayes, Random Forest, and Support Vector Machine (SVM)), what are the plans that the understudy is qualified for are predicted. The accuracy of the three computations is the basis of an investigation. The accuracy of the three calculations is lowered, and the effective calculation that produces the most accurate end result is finally used to play out the forecast of the plans that a certain understudy is qualified for. Finally, the understudies are given the anticipated plans predicted using the most elevated effective calculation among the three computations. As a result of this attempt, country understudies will be able to return to consider completely various recipient plans provided by the government, which they will employ for the advancement of the country's environmental elements.

Author (s) Details

Dr. S. Maheswari
National Engineering College, Kovilpatti, Tamil Nadu, India.


M. Manochitra
National Engineering College, Kovilpatti, Tamil Nadu, India.

D. Thamarai Selvi
National Engineering College, Kovilpatti, Tamil Nadu, India.

View Book :-  https://stm.bookpi.org/AAER-V12/article/view/1280