scholarly journals Case-Based Reasoning Framework for Malaria Diagnosis

2020 ◽  
Vol 12 (6) ◽  
pp. 31-48
Author(s):  
Eshetie Gizachew Addisu ◽  
◽  
Abiot Sinamo Boltena ◽  
Samson Yohannes Amare
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Malaria Diagnosis ◽  
Retrieval Algorithm ◽  
Case Based Reasoning ◽  
Learning Approach ◽  
Tigray Region ◽  
Programming Tool ◽  
Machine Learning Approach ◽  
Case Based

Malaria is life threatening disease in Ethiopia specifically in Tigray region. Having common symptoms with other diseases makes it complex and challenging to diagnose effectively. In this paper case based reasoning framework for malaria diagnosis has been designed to diminish the challenges faced by inexperienced practitioners during malaria diagnosis and to solve the problem on shortage of health professionals. The required knowledge for this study was collected through interview and document analysis from domain experts, malaria patient history cards and other related relevant documents. In the case acquisition process the manual format of cases makes the process too challenging. Decision tree is used to model the acquired knowledge. The case structure was then constructed using the selected most determinant attributes. Machine learning approach is applied to select the most relevant features. Feature-vector case representation technique is applied to represent the collected malaria cases. Jcolibri programming tool integrated with Eclipse and Nearest Neighbor retrieval algorithm are used to design the framework. To the end based on the results we can say that the machine learning approach can be used to select most relevant attributes in diseases having several common symptoms and designing case-based diagnosis frameworks could overcome the main problems observed in health centers of Tigray. As an artifact the framework is evaluated by statistical analysis, comparative evaluation, user evaluation and other evaluation techniques. Averagely 79 % precision, 89 % recall, 91.4% accuracy and 78.8% domain expert’s evaluation was the results scored.

The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach

Mekatronika ◽  
2020 ◽  
Vol 2 (2) ◽  
pp. 1-12
Author(s):  
Muhammad Nur Aiman Shapiee ◽  
Muhammad Ar Rahim Ibrahim ◽  
Muhammad Amirul Abdullah ◽  
Rabiu Muazu Musa ◽  
Noor Azuan Abu Osman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Olympic Games ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Test Dataset ◽  
Machine Learning Approach ◽  
Competitive Games

The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of  three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision.

Sentiment Analysis for Iraqis Dialect in Social Media

2018 ◽  
Vol 1 (2) ◽  
pp. 24-32
Author(s):  
Lamiaa Abd Habeeb
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Learning Approach ◽  
Machine Learning Algorithm ◽  
K Nearest Neighbor ◽  
Multinomial Models ◽  
Ensemble Machine Learning ◽  
Machine Learning Approach

In this paper, we designed a system that extract citizens opinion about Iraqis government and Iraqis politicians through analyze their comments from Facebook (social media network). Since the data is random and contains noise, we cleaned the text and builds a stemmer to stem the words as much as possible, cleaning and stemming reduced the number of vocabulary from 28968 to 17083, these reductions caused reduction in memory size from 382858 bytes to 197102 bytes. Generally, there are two approaches to extract users opinion; namely, lexicon-based approach and machine learning approach. In our work, machine learning approach is applied with three machine learning algorithm which are; Naïve base, K-Nearest neighbor and AdaBoost ensemble machine learning algorithm. For Naïve base, we apply two models; Bernoulli and Multinomial models. We found that, Naïve base with Multinomial models give highest accuracy.

scholarly journals A Machine Learning Approach to Predict Customer Usage of a Home Workout Platform

2021 ◽  
Vol 11 (21) ◽  
pp. 9927
Author(s):  
Qiuying Chen ◽  
SangJoon Lee
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nearest Neighbor ◽  
Important Variable ◽  
Receiver Operating Curve ◽  
Support Vector ◽  
Learning Approach ◽  
K Nearest Neighbor ◽  
Machine Learning Approach ◽  
Supervised Learning Algorithms

Health authorities have recommended the use of digital tools for home workouts to stay active and healthy during the COVID-19 pandemic. In this paper, a machine learning approach is proposed to assess the activity of users on a home workout platform. Keep is a home workout application dedicated to providing one-stop exercise solutions such as fitness teaching, cycling, running, yoga, and fitness diet guidance. We used a data crawler to collect the total training set data of 7734 Keep users and compared four supervised learning algorithms: support vector machine, k-nearest neighbor, random forest, and logistic regression. The receiver operating curve analysis indicated that the overall discrimination verification power of random forest was better than that of the other three models. The random forest model was used to classify 850 test samples, and a correct rate of 88% was obtained. This approach can predict the continuous usage of users after installing the home workout application. We considered 18 variables on Keep that were expected to affect the determination of continuous participation. Keep certification is the most important variable that affected the results of this study. Keep certification refers to someone who has verified their identity information and can, therefore, obtain the Keep certification logo. The results show that the platform still needs to be improved in terms of real identity privacy information and other aspects.

scholarly journals Machine learning approach for prediction of hepatic enhancement during the hepatobiliary phase of Gd-EOB DTPA-enhanced MRI.

2021 ◽  
Author(s):  
Ji Su Ko ◽  
Jieun Byun ◽  
Seongkeun Park ◽  
Ji Young Woo
Keyword(s):  
Machine Learning ◽  
Liver Disease ◽  
Predictive Model ◽  
Nearest Neighbor ◽  
Area Under The Curve ◽  
Learning Approach ◽  
Hepatobiliary Phase ◽  
K Nearest Neighbor ◽  
Enhanced Mri ◽  
Machine Learning Approach

Abstract We retrospectively assessed 214 patients with chronic liver disease or liver cirrhosis who underwent magnetic resonance imaging (MRI) enhanced with gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) from August 2016 to May 2020 to evaluate the relationship between biochemical results that reflect liver function and hepatic enhancement. With the information gained we employed a machine learning approach with the K-Nearest Neighbor (KNN) algorithm to develop a predictive model for determining insufficient hepatic enhancement during the hepatobiliary phase (HBP) in Gd-EOB-DTPA-enhanced MRI. Using both quantitative and qualitative assessments, the total bilirubin (TB), albumin (Alb), prothrombin time-international normalized ratio, platelet, Child-Pugh score (CPS), and Model for End-stage Liver Disease Sodium (MELD-Na) score were related to decreased hepatic enhancement. In a multivariate analysis, TB and Alb were associated with insufficient enhancement (p < 0.001). The predictive model showed that a combination of a variety of biochemical parameters had better performance (accuracy = 82.8%, area under the curve (AUC) = 0.861) in predicting insufficient enhancement than either the CPS (accuracy = 79.5%, AUC = 0.845) or the MELD-Na score (accuracy = 80.8%, AUC = 0.821). By using a machine-learning-based predictive model with the KNN algorithm, radiologists can predict insufficient hepatic enhancement during HBP in advance and adjust each patient's individually optimized MRI protocol.

A hybrid machine learning approach of fuzzy-rough-k-nearest neighbor, latent semantic analysis, and ranker search for efficient disease diagnosis

2021 ◽  
pp. 1-16
Author(s):  
Sunil Kumar Jha ◽  
Ninoslav Marina ◽  
Jinwei Wang ◽  
Zulfiqar Ahmad
Keyword(s):  
Machine Learning ◽  
Latent Semantic Analysis ◽  
Semantic Analysis ◽  
Nearest Neighbor ◽  
Disease Diagnosis ◽  
Learning Approach ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Machine Learning Approach ◽  
Hybrid Machine

Machine learning approaches have a valuable contribution in improving competency in automated decision systems. Several machine learning approaches have been developed in the past studies in individual disease diagnosis prediction. The present study aims to develop a hybrid machine learning approach for diagnosis predictions of multiple diseases based on the combination of efficient feature generation, selection, and classification methods. Specifically, the combination of latent semantic analysis, ranker search, and fuzzy-rough-k-nearest neighbor has been proposed and validated in the diagnosis prediction of the primary tumor, post-operative, breast cancer, lymphography, audiology, fertility, immunotherapy, and COVID-19, etc. The performance of the proposed approach is compared with single and other hybrid machine learning approaches in terms of accuracy, analysis time, precision, recall, F-measure, the area under ROC, and the Kappa coefficient. The proposed hybrid approach performs better than single and other hybrid approaches in the diagnosis prediction of each of the selected diseases. Precisely, the suggested approach achieved the maximum recognition accuracy of 99.12%of the primary tumor, 96.45%of breast cancer Wisconsin, 94.44%of cryotherapy, 93.81%of audiology, and significant improvement in the classification accuracy and other evaluation metrics in the recognition of the rest of the selected diseases. Besides, it handles the missing values in the dataset effectively.

scholarly journals Implementasi Case Based Reasoning Mendiagnosa Penyakit Stroke Menggunakan Algoritma Probabilistic Symmetric

2021 ◽  
Vol 8 (1) ◽  
pp. 10-16
Author(s):  
Rizal Rachman
Keyword(s):  
Machine Learning ◽  
Automated Reasoning ◽  
Nearest Neighbor ◽  
Case Based Reasoning ◽  
K Nearest Neighbor ◽  
Case Based

Penyakit stroke merupakan salah satu penyakit yang cukup berbahaya di Indonesia. Penyakit yang diawali dengan tanda seperti mati rasa yang berada di wajah, kaki, lengan, ataupun di sisi salah satu tubuh, disertai dengan adanya kebingungan dan sulit untuk bicara. Penyakit stroke merupakan suatu penyakit yang berhubungan dengan aliran darah ke otak. Mengingat sedikitnya dokter spesialis saraf di berbagai daerah di Indonesia serta keterbatasan waktu dan tenaga seorang dokter dalam melayani masyarakat luas sehingga dibutuhkan sebuah sistem yang mampu membantu mendiagnosa penyakit stroke. Sistem pakar dapat memungkinkan orang awam bisa mengerjakan pekerjaan para ahli. Perhitungan ketidakpastian dalam sistem pakar dapat dilakukan dengan beberapa metode ketidakpastian. Case based reasoning merupakan sebuah paradigma utama dalam penalaran otomatis (automated reasoning) dan mesin pembelajaran (machine learning). Untuk mencari jarak terdekat dari tiap tiap kasus dan mencari ukuran kemiripan (similaritas) kasus lama dengan kasus baru, dalam penelitian ini menggunakan algoritma K-Nearest Neighbor dan algoritma similaritas probabilistic symmetric. Dalam penelitian ini, telah dibuat web aplikasi sistem pakar diagnosa penyakit stroke sehingga dapat membantu masyarakat/pengguna dalam mendiagnosa awal gejala penyakit stroke tanpa menemui seorang dokter.

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