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Effect of dimension reduction with PCA and machine learning algorithms on diabetes diagnosis performance 利用 PCA 和机器学习算法降维对糖尿病诊断性能的影响

Turkish Journal of Engineering Pub Date : 2024-07-05 DOI: 10.31127/tuje.1413087

Yavuz Bahadir Koca, Elif Aktepe

{"title":"Effect of dimension reduction with PCA and machine learning algorithms on diabetes diagnosis performance","authors":"Yavuz Bahadir Koca, Elif Aktepe","doi":"10.31127/tuje.1413087","DOIUrl":"https://doi.org/10.31127/tuje.1413087","url":null,"abstract":"Diabetes, a long-term metabolic disorder, causes persistently high blood sugar and presents a significant global health challenge. Early diagnosis is of vital importance in mitigating the effects of diabetes. This study aims to investigate diabetes diagnosis and risk prediction using a comprehensive diabetes dataset created in 2023. The dataset contains clinical and anthropometric data of patients. Data simplification was successfully applied to clean unnecessary information and reduce data dimensionality. Additionally, methods like Principal Component Analysis were applied to decrease the number of variables in the dataset. These analyses rendered the dataset more manageable and improved its performance. In this study, a dataset encompassing health data of a total of 100,000 individuals was utilized. This dataset consists of 8 input features and 1 output feature. The primary objective is to determine the algorithm that exhibits the best performance for diabetes diagnosis. There was no missing data during the data preprocessing stage, and the necessary transformations were carried out successfully. Nine different machine learning algorithms were applied to the dataset in this study. Each algorithm employed various modelling approaches to evaluate its performance in diagnosing diabetes. The results demonstrate that machine learning models are successful in predicting the presence of diabetes and the risk of developing it in healthy individuals. Particularly, the random forest model provided superior results across all performance metrics. This study provides significant findings that can shed light on future research in diabetes diagnosis and risk prediction. Dimensionality reduction techniques have proven to be valuable in data analysis and have highlighted the potential to facilitate diabetes diagnosis, thereby enhancing the quality of life for patients.","PeriodicalId":518565,"journal":{"name":"Turkish Journal of Engineering","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141674291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Detection of cotton leaf disease with machine learning model 用机器学习模型检测棉花叶病

Turkish Journal of Engineering Pub Date : 2024-04-18 DOI: 10.31127/tuje.1406755

Unain Hyder, Mir Rahib Hussain

{"title":"Detection of cotton leaf disease with machine learning model","authors":"Unain Hyder, Mir Rahib Hussain","doi":"10.31127/tuje.1406755","DOIUrl":"https://doi.org/10.31127/tuje.1406755","url":null,"abstract":"This study aims to use a machine learning (ML) model to accurately classify four datasets of cotton crop leaves as either infected or healthy. Bacterial blight, Curly virus, Fussarium Wilt, and healthy leaves were used as the datasets for the study. ML is a useful tool in detecting cotton leaf diseases and can minimize the rate of disease. The problem is that without machine learning technique it is very difficult and time consuming to detect the diseases then to sort out this problem a machine learning model is proposed and to test the accuracy of the proposed model, the confusion matrix concept was used. The researchers have done their research works to diagnose the diseases by using (ML) model but the drawback of their research was that the results which were given by the different (ML) models were not accurate. The target of the study was to identify diseases affecting the cotton plant in the early stages using traditional techniques. However, utilizing various image processing techniques and machine learning algorithms, including a convolutional neural network, proved to be helpful in diagnosing the diseases. This technological approach can simplify the detection of damaged leaves and minimize the efforts of farmers in detecting those diseases. Cotton is a natural fiber produced on a large scale, and it is grown on 2.5% of overall agronomic land. The detection of cotton leaf diseases is crucial to maintain the crop's productivity and provide reliable earnings to farmers. A confusion matrix is N X N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by machine learning model. This technique has four parameters to test the accuracy of the results which is given in my research work.","PeriodicalId":518565,"journal":{"name":"Turkish Journal of Engineering","volume":" 8","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140686711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A lasso regression-based forecasting model for daily gasoline consumption: Türkiye Case 基于套索回归的日汽油消耗量预测模型：土耳其案例

Turkish Journal of Engineering Pub Date : 2024-01-04 DOI: 10.31127/tuje.1354501

Ertugrul Ayyıldız, Mirac Murat

引用次数: 0