Education 4.0: Teaching the Basics of KNN, LDA and Simple Perceptron Algorithms for Binary Classification Problems

One of the main focuses of Education 4.0 is to provide students with knowledge on disruptive technologies, such as Machine Learning (ML), as well as the skills to implement this knowledge to solve real-life problems. Therefore, both students and professors require teaching and learning tools that facilitate the introduction to such topics. Consequently, this study looks forward to contributing to the development of those tools by introducing the basic theory behind three machine learning classifying algorithms: K-Nearest-Neighbor (KNN), Linear Discriminant Analysis (LDA), and Simple Perceptron; as well as discussing the diverse advantages and disadvantages of each method. Moreover, it is proposed to analyze how these methods work on different conditions through their implementation over a test bench. Thus, in addition to the description of each algorithm, we discuss their application to solving three different binary classification problems using three different datasets, as well as comparing their performances in these specific case studies. The findings of this study can be used by teachers to provide students the basic knowledge of KNN, LDA, and perceptron algorithms, and, at the same time, it can be used as a guide to learn how to apply them to solve real-life problems that are not limited to the presented datasets.

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Application of Machine Learning Approaches for the Design and Study of Anticancer Drugs

Current Drug Targets ◽

10.2174/1389450119666180809122244 ◽

2019 ◽

Vol 20 (5) ◽

pp. 488-500 ◽

Cited By ~ 6

Author(s):

Yan Hu ◽

Yi Lu ◽

Shuo Wang ◽

Mengying Zhang ◽

Xiaosheng Qu ◽

...

Keyword(s):

Machine Learning ◽

Drug Design ◽

Anticancer Drugs ◽

Nearest Neighbor ◽

Cost Effective ◽

Support Vector ◽

Learning Approaches ◽

K Nearest Neighbor ◽

Activity Prediction ◽

Linear Discriminant

Background: Globally the number of cancer patients and deaths are continuing to increase yearly, and cancer has, therefore, become one of the world's highest causes of morbidity and mortality. In recent years, the study of anticancer drugs has become one of the most popular medical topics. Objective: In this review, in order to study the application of machine learning in predicting anticancer drugs activity, some machine learning approaches such as Linear Discriminant Analysis (LDA), Principal components analysis (PCA), Support Vector Machine (SVM), Random forest (RF), k-Nearest Neighbor (kNN), and Naïve Bayes (NB) were selected, and the examples of their applications in anticancer drugs design are listed. Results: Machine learning contributes a lot to anticancer drugs design and helps researchers by saving time and is cost effective. However, it can only be an assisting tool for drug design. Conclusion: This paper introduces the application of machine learning approaches in anticancer drug design. Many examples of success in identification and prediction in the area of anticancer drugs activity prediction are discussed, and the anticancer drugs research is still in active progress. Moreover, the merits of some web servers related to anticancer drugs are mentioned.

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Cardiovascular Disease Prediction from Electrocardiogram by using Machine Learning Method: A Snapshot from the Subjects of the Malaysian Cohort

10.21203/rs.2.22561/v1 ◽

2020 ◽

Author(s):

Nazrul Anuar Nayan ◽

Hafifah Ab Hamid ◽

Mohd Zubir Suboh ◽

Noraidatulakma Abdullah ◽

Rosmina Jaafar ◽

...

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Nearest Neighbor ◽

Total Cholesterol Level ◽

Population Based ◽

Support Vector ◽

K Nearest Neighbor ◽

Cvd Risk ◽

Linear Discriminant ◽

Artificial Neural Network Ann

Abstract Background: Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. Results: This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. Conclusions: In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.

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A Study on the Psychological Analysis System Using Machine Learning

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i3.33.18591 ◽

2018 ◽

Vol 7 (3.33) ◽

pp. 128

Author(s):

Ki Young Lee ◽

Kyu Ho Kim ◽

Jeong Jin Kang ◽

Sung Jai Choi ◽

Yong Soon Im ◽

...

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Smart Phone ◽

Principal Component ◽

Digital Data ◽

Expression Recognition ◽

K Nearest Neighbor ◽

Linear Discriminant ◽

Psychological Analysis ◽

Analysis System

Real-time facial expression recognition and analysis technology is recently drawing attention in areas of computer vision, computer graphics, and HCI. Recognition of user’s emotion on the basis of video and voice is drawing particular interest. The technology may help managers of households or hospitals. In the present study, video and voice were converted into digital data through MATLAB by using PCA(Principal Component Analysis), LDA(Linear Discriminant Analysis), KNN(K Nearest Neighbor) algorithms to analyze emotions through machine learning. The manager of the psychological analysis counseling system may understand a user’s emotion in an smart phone environment. This system of the present study may help the manager to have a smooth conversation or develop a smooth relationship with a user on the basis of the provided psychological analysis results.

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Cardiovascular Disease Prediction from Electrocardiogram by Using Machine Learning

International Journal of Online and Biomedical Engineering (iJOE) ◽

10.3991/ijoe.v16i07.13569 ◽

2020 ◽

Vol 16 (07) ◽

pp. 34

Author(s):

Nayan Nazrul Anuar ◽

Ab Hamid Hafifah ◽

Suboh Mohd Zubir ◽

Abdullah Noraidatulakma ◽

Jaafar Rosmina ◽

...

Keyword(s):

Machine Learning ◽

Cardiovascular Disease ◽

Nearest Neighbor ◽

Total Cholesterol Level ◽

Population Based ◽

Support Vector ◽

K Nearest Neighbor ◽

Cvd Risk ◽

Linear Discriminant ◽

Artificial Neural Network Ann

Cardiovascular disease (CVD) is the leading cause of deaths worldwide. In 2017, CVD contributed to 13,503 deaths in Malaysia. The current approaches for CVD prediction are usually invasive and costly. Machine learning (ML) techniques allow an accurate prediction by utilizing the complex interactions among relevant risk factors. This study presents a case–control study involving 60 participants from The Malaysian Cohort, which is a prospective population-based project. Five parameters, namely, the R–R interval and root mean square of successive differences extracted from electrocardiogram (ECG), systolic and diastolic blood pressures, and total cholesterol level, were statistically significant in predicting CVD. Six ML algorithms, namely, linear discriminant analysis, linear and quadratic support vector machines, decision tree, k-nearest neighbor, and artificial neural network (ANN), were evaluated to determine the most accurate classifier in predicting CVD risk. ANN, which achieved 90% specificity, 90% sensitivity, and 90% accuracy, demonstrated the highest prediction performance among the six algorithms. In summary, by utilizing ML techniques, ECG data can serve as a good parameter for CVD prediction among the Malaysian multiethnic population.

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Predicting Stock Exchange using Supervised Learning Algorithms

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a4144.119119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 4081-4090

Keyword(s):

Machine Learning ◽

Random Forest ◽

Stock Market ◽

Supervised Learning ◽

Nearest Neighbor ◽

Market Price ◽

Real Life ◽

Support Vector ◽

K Nearest Neighbor ◽

Future Value

The stock market price trend is one of the brightest areas in the field of computer science, economics, finance, administration, etc. The stock market forecast is an attempt to determine the future value of the equity traded on a financial transaction with another financial system. The current work clearly describes the prediction of a stock using Machine Learning. The adoption of machine learning and artificial intelligence techniques to predict the prices of the stock is a growing trend. More and more researchers invest their time every day in coming up with ways to arrive at techniques that can further improve the accuracy of the stock prediction model. This paper is mainly concerned with the best model to predict the stock market value. During the mechanism of contemplating the various techniques and variables that can be taken into consideration, we discovered five models Which are based on supervised learning techniques i.e.., Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), Bernoulli Naïve Bayes.The empirical results show that SVC performs the best for large datasets and Random Forest, Naïve Bayes is the best for small datasets. The successful prediction for the stock will be a great asset for the stock The stock market price trend is one of the brightest areas in the field of computer science, economics, finance, administration, etc. The stock market forecast is an attempt to determine the future value of the equity traded on a financial transaction with another financial system. The current work clearly describes the prediction of a stock using Machine Learning. The adoption of machine learning and artificial intelligence techniques to predict the prices of the stock is a growing trend. More and more researchers invest their time every day in coming up with ways to arrive at techniques that can further improve the accuracy of the stock prediction model. This paper is mainly concerned with the best model to predict the stock market value. During the mechanism of contemplating the various techniques and variables that can be taken into consideration, we discovered five models Which are based on supervised learning techniques i.e.., Support Vector Machine (SVM), Random Forest, K-Nearest Neighbor (KNN), Bernoulli Naïve Bayes.The empirical results show that SVC performs the best for large datasets and Random Forest, Naïve Bayes is the best for small datasets. The successful prediction for the stock will be a great asset for the stock market institutions and will provide real-life solutions to the problems that stock investors face.market institutions and will provide real-life solutions to the problems that stock investors face.

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Screening of Mood Symptoms Using MMPI-2-RF Scales: An Application of Machine Learning Techniques

Journal of Personalized Medicine ◽

10.3390/jpm11080812 ◽

2021 ◽

Vol 11 (8) ◽

pp. 812

Author(s):

Sunhae Kim ◽

Hye-Kyung Lee ◽

Kounseok Lee

Keyword(s):

Machine Learning ◽

Depressive Symptoms ◽

Nearest Neighbor ◽

Common Mental Disorders ◽

Machine Learning Techniques ◽

K Nearest Neighbor ◽

Linear Discriminant ◽

Random Forest Classification ◽

Mood Symptoms ◽

Learning Analysis

(1) Background: The MMPI-2-RF is the most widely used and most researched test among the tools for assessing psychopathology, and previous studies have established its validity. Mood disorders are the most common mental disorders worldwide; they present difficulties in early detection, go undiagnosed in many cases, and have a poor prognosis. (2) Methods: We analyzed a total of 8645 participants. We used the PHQ-9 to evaluate depressive symptoms and the MDQ to evaluate hypomanic symptoms. We used the 10 MMPI-2 Restructured Form scales and 23 Specific Problems scales for the MMPI-2-RF as predictors. We performed machine learning analysis using the k-nearest neighbor classification, linear discriminant analysis, and random forest classification. (3) Results: Through the machine learning technique, depressive symptoms were predicted with an AUC of 0.634–0.767, and the corresponding value range for hypomanic symptoms was 0.770–0.840. When using RCd to predict depressive symptoms, the AUC was 0.807, but this value was 0.840 when using linear discriminant classification. When predicting hypomanic symptoms with RC9, the AUC was 0.704, but this value was 0.767 when using the linear discriminant method. (4) Conclusions: Using machine learning analysis, we defined that participants’ mood symptoms could be classified and predicted better than when using the Restructured Clinical scales.

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Fruit Classification Utilizing a Robotic Gripper with Integrated Sensors and Adaptive Grasping

Mathematical Problems in Engineering ◽

10.1155/2021/7157763 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Jintao Zhang ◽

Shuang Lai ◽

Huahua Yu ◽

Erjie Wang ◽

Xizhe Wang ◽

...

Keyword(s):

Machine Learning ◽

Information Acquisition ◽

Fruits And Vegetables ◽

Nearest Neighbor ◽

Machine Learning Algorithms ◽

Support Vector ◽

Tactile Sensing ◽

K Nearest Neighbor ◽

Linear Discriminant ◽

Agricultural Robots

As the core component of agricultural robots, robotic grippers are widely used for plucking, picking, and harvesting fruits and vegetables. Secure grasping is a severe challenge in agricultural applications because of the variation in the shape and hardness of agricultural products during maturation, as well as their variety and delicacy. In this study, a fruit identification method utilizing an adaptive gripper with tactile sensing and machine learning algorithms is reported. An adaptive robotic gripper is designed and manufactured to perform adaptive grasping. A tactile sensing information acquisition circuit is built, and force and bending sensors are integrated into the robotic gripper to measure the contact force distribution on the contact surface and the deformation of the soft fingers. A robotic manipulator platform is developed to collect the tactile sensing data in the grasping process. The performance of the random forest (RF), k-nearest neighbor (KNN), support vector classification (SVC), naive Bayes (NB), linear discriminant analysis (LDA), and ridge regression (RR) classifiers in identifying and classifying five types of fruits using the adaptive gripper is evaluated and compared. The RF classifier achieves the highest accuracy of 98%, while the accuracies of the other classifiers vary from 74% to 97%. The experiment illustrates that efficient and accurate fruit identification can be realized with the adaptive gripper and machine learning classifiers, and that the proposed method can provide a reference for controlling the grasping force and planning the robotic motion in the plucking, picking, and harvesting of fruits and vegetables.

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Implementation of machine learning algorithms to create diabetic patient re-admission profiles

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-019-0990-x ◽

2019 ◽

Vol 19 (S9) ◽

Cited By ~ 3

Author(s):

Mohamed Alloghani ◽

Ahmed Aljaaf ◽

Abir Hussain ◽

Thar Baker ◽

Jamila Mustafina ◽

...

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Machine Learning Algorithms ◽

Support Vector ◽

Diabetic Patients ◽

K Nearest Neighbor ◽

Critical Approach ◽

Linear Discriminant ◽

Applied Machine Learning ◽

Learning Machine

Abstract Background Machine learning is a branch of Artificial Intelligence that is concerned with the design and development of algorithms, and it enables today’s computers to have the property of learning. Machine learning is gradually growing and becoming a critical approach in many domains such as health, education, and business. Methods In this paper, we applied machine learning to the diabetes dataset with the aim of recognizing patterns and combinations of factors that characterizes or explain re-admission among diabetes patients. The classifiers used include Linear Discriminant Analysis, Random Forest, k–Nearest Neighbor, Naïve Bayes, J48 and Support vector machine. Results Of the 100,000 cases, 78,363 were diabetic and over 47% were readmitted.Based on the classes that models produced, diabetic patients who are more likely to be readmitted are either women, or Caucasians, or outpatients, or those who undergo less rigorous lab procedures, treatment procedures, or those who receive less medication, and are thus discharged without proper improvements or administration of insulin despite having been tested positive for HbA1c. Conclusion Diabetic patients who do not undergo vigorous lab assessments, diagnosis, medications are more likely to be readmitted when discharged without improvements and without receiving insulin administration, especially if they are women, Caucasians, or both.

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Machine Learning Methods for COVID-19 Prediction Using Human Genomic Data

Proceedings ◽

10.3390/proceedings2021074020 ◽

2021 ◽

Vol 74 (1) ◽

pp. 20

Author(s):

Hilal Arslan

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Binary Classification ◽

Machine Learning Techniques ◽

Support Vector ◽

Viral Gene ◽

K Nearest Neighbor ◽

Accurate Identification ◽

Infected People ◽

Novel Coronavirus

Accurate identification of COVID-19 is now a critical task since it has seriously damaged daily life, public health, and the economy. It is essential to identify the infected people to prevent the further spread of the pandemic and to treat infected patients quickly. Machine learning techniques have a significant role in predicting of COVID-19. In this study, we performed binary classification (COVID-19 vs. other types of coronavirus) by extracting features from genome sequences. Support vector machines, naive Bayes, K-nearest neighbor, and random forest methods were used for classification. We used viral gene sequences from the 2019 Novel Coronavirus Resource Database. Experimental results presented show that a decision tree method achieved 93% accuracy.

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Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods

Academic Pathology ◽

10.1177/2374289519873088 ◽

2019 ◽

Vol 6 ◽

pp. 237428951987308 ◽

Cited By ~ 25

Author(s):

Hooman H. Rashidi ◽

Nam K. Tran ◽

Elham Vali Betts ◽

Lydia P. Howell ◽

Ralph Green

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Nearest Neighbor ◽

Health Care Research ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Basic Knowledge ◽

Support Vector ◽

K Nearest Neighbor ◽

Supervised Methods

Increased interest in the opportunities provided by artificial intelligence and machine learning has spawned a new field of health-care research. The new tools under development are targeting many aspects of medical practice, including changes to the practice of pathology and laboratory medicine. Optimal design in these powerful tools requires cross-disciplinary literacy, including basic knowledge and understanding of critical concepts that have traditionally been unfamiliar to pathologists and laboratorians. This review provides definitions and basic knowledge of machine learning categories (supervised, unsupervised, and reinforcement learning), introduces the underlying concept of the bias-variance trade-off as an important foundation in supervised machine learning, and discusses approaches to the supervised machine learning study design along with an overview and description of common supervised machine learning algorithms (linear regression, logistic regression, Naive Bayes, k-nearest neighbor, support vector machine, random forest, convolutional neural networks).

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