scholarly journals A Predictive Model for Kidney Transplant Graft Survival using Machine Learning

2020 ◽  
Author(s):  
Eric S. Pahl ◽  
W. Nick Street ◽  
Hans J. Johnson ◽  
Alan I. Reed
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Cox Regression ◽  
Risk Index ◽  
Error Rates ◽  
Machine Learning Method ◽  
Learning Method ◽  
Kaplan Meier ◽  
End Stage

Kidney transplantation is the best treatment for end-stage renal failure patients. The predominant method used for kidney quality assessment is the Cox regression-based, kidney donor risk index. A machine learning method may provide improved prediction of transplant outcomes and help decision-making. A popular tree-based machine learning method, random forest, was trained and evaluated with the same data originally used to develop the risk index (70,242 observations from 1995-2005). The random forest successfully predicted an additional 2,148 transplants than the risk index with equal type II error rates of 10%. Predicted results were analyzed with follow-up survival outcomes up to 240 months after transplant using Kaplan-Meier analysis and confirmed that the random forest performed significantly better than the risk index (p<0.05). The random forest predicted significantly more successful and longer-surviving transplants than the risk index. Random forests and other machine learning models may improve transplant decisions.

scholarly journals Analisis Sentimen Terhadap Review Film Menggunakan Metode Modified Balanced Random Forest dan Mutual Information

2021 ◽  
Vol 5 (2) ◽  
pp. 415
Author(s):  
Firdausi Nuzula Zamzami ◽  
Adiwijaya Adiwijaya ◽  
Mahendra Dwifebri P
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Mutual Information ◽  
Sentiment Analysis ◽  
Information Exchange ◽  
The Internet ◽  
Machine Learning Method ◽  
Learning Method ◽  
Internet Information

Information exchange is currently the most happening on the internet. Information exchange can be done in many ways, such as expressing expressions on social media. One of them is reviewing a film. When someone reviews a film he will use his emotions to express their feelings, it can be positive or negative. The fast growth of the internet has made information more diverse, plentiful and unstructured. Sentiment analysis can handle this, because sentiment analysis is a classification process to understand opinions, interactions, and emotions of a document or text that is carried out automatically by a computer system. One suitable machine learning method is the Modified Balanced Random Forest. To deal with the various data, the feature selection used is Mutual Information. With these two methods, the system is able to produce an accuracy value of 79% and F1-scores value of 75%.

scholarly journals Bilateral Globus Pallidus Internus Deep Brain Stimulation for Parkinson’s Disease: Therapeutic Effects and Motor Outcomes Prediction

2020 ◽  
Author(s):  
Dingding Shen ◽  
Linhao Cao ◽  
Yun Ling ◽  
Dianyou Li ◽  
Kang Ren ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Brain Stimulation ◽  
Prediction Model ◽  
Machine Learning Method ◽  
Learning Method ◽  
One Year ◽  
Deep Brain

Abstract Background: Deep brain stimulation (DBS) has emerged as a highly effective surgical treatment for advanced Parkinson’s disease (PD). Good response in levodopa challenge test has suggested as criterion to identify optimal candidates for surgery. However, the response to levodopa and DBS is not always congruent, and predictive value of the levodopa test remains controversial. This study was set out to identify predictors of response to DBS and develop a novel prediction model evaluating DBS candidacy. Methods: Herein, we retrospectively analyzed 62 consecutive PD patients who underwent bilateral globus pallidus interna (GPi) DBS from 2016 to 2019.  The changes in UPDRS-III (Unified Parkinson’s Disease Rating Scale part III) total and subscores after surgery at one-year follow-up were evaluated and potential predictor variables were also collected. In the training cohort of 29 patients, we developed a novel machine learning method with 5-fold cross validations implementing on these variables to predict GPi DBS treatment outcomes in a multivariate linear analysis. Furthermore, the machine learning model was externally validated with another cohort of 33 GPi DBS PD patients.Results: GPi DBS significantly improved postoperative motor function of PD patients. The overall UPDRS-III scores improved by 30.4%, with highest improvement in tremor (75.0%), followed by limb bradykinesia (27.5%), rigidity (27.3%) and axial bradykinesia (22.4%). Most intriguingly, improvement in tremor can be predicted with high accuracy using this prediction model (adjusted R2= 0.82 for absolute improvement, and adjusted R2 = 0.76 for relative improvement), in which off medication tremor subscore was identified as the most powerful preoperative predictor. In the external validation cohort, the machine learning method showed good predictive performance.Conclusions: We confirmed the effects of bilateral GPi-DBS with a one-year follow-up. The good performance of the present prediction model demonstrated the utility of machine-learning based motor response prediction after GPi DBS, based on clinical preoperative variables.

scholarly journals Effective Compatibility and Reduction of Data for Bigdata Applications

2019 ◽  
Vol 9 (1) ◽  
pp. 3781-3784
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Language Processing ◽  
Processing Technique ◽  
Unstructured Data ◽  
Machine Learning Method ◽  
Learning Method ◽  
Source File ◽  
Natural Language Processing Technique ◽  
Input And Output

The system identifies a duplicate record from the database using the machine learning method. We must pass unstructured data. Data are prepared using any natural language processing technique such as text similarity. This prepared data is then fed into the latest machine learning method called Random Forest. After this data collection, using these files, the target file is compared to the source file. We make input and output files. This is carried out until accurate efficiency is generated

scholarly journals Tropical Overshooting Cloud-Top Height Retrieval from Himawari-8 Imagery Based on Random Forest Model

Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 173
Author(s):  
Gaoyun Wang ◽  
Hongqing Wang ◽  
Yizhou Zhuang ◽  
Qiong Wu ◽  
Siyue Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Brightness Temperature ◽  
Zenith Angle ◽  
Single Channel ◽  
Interpolation Method ◽  
Moisture Distribution ◽  
Strong Impact ◽  
Machine Learning Method ◽  
Learning Method

Tropical overshooting convection has a strong impact on both heat budget and moisture distribution in the upper troposphere and lower stratosphere, and it can pose a great risk to aviation safety. Cloud-top height is one of the essential concerns of overshooting convection for both the climate system and the aviation weather forecast. The main purpose of our work is to verify the application of the machine learning method, taking the random forest (RF) model as an instance, in overshooting cloud-top height retrieval from Himawari-8 data. By using collocated CloudSat observations as a reference, we utilize several infrared indicators of Himawari-8 that are commonly recognized to relate to cloud-top height, along with some temporal and geographical parameters (latitude, month, satellite zenith angle, etc.), as predictors to construct and validate the model. Analysis of variable importance shows that the brightness temperature of 6.2 um acts as the dominant predictor, followed by satellite zenith angle, brightness temperature of 13.3 um, latitude, and month. In the comparison between the RF model and the traditional single-channel interpolation method, retrievals from the RF model agree well with observation with a high correlation coefficient (0.92), small RMSE (222 m), and small MAE (164 m), while these metrics from traditional single-channel interpolation method shows lower skills (0.70, 1305 m, and 1179 m). This work presents a new sight of overshooting cloud-top height retrieval based on the machine learning method.

scholarly journals Machine Learning Method for Continuous Noninvasive Blood Pressure Detection Based on Random Forest

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 34112-34118
Author(s):  
Xiaohui Chen ◽  
Shuyang Yu ◽  
Yongfang Zhang ◽  
Fangfang Chu ◽  
Bin Sun
Keyword(s):  
Machine Learning ◽  
Blood Pressure ◽  
Random Forest ◽  
Machine Learning Method ◽  
Learning Method ◽  
Noninvasive Blood Pressure

Data Mining From Endmill Tool Catalog Information Based on the Use of a Machine Learning Method

2020 ◽  
Author(s):  
Akihito Asakura ◽  
Toshiki Hirogaki ◽  
Eiichi Aoyama ◽  
Hiroyuki Kodama
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Large Scale ◽  
Manufacturing Industry ◽  
New Technologies ◽  
Cutting Conditions ◽  
Machine Learning Method ◽  
Learning Method ◽  
Large Scale Data ◽  
Recent Computer

Abstract In recent years, the needs associated with the development of new technologies in the manufacturing industry that utilize big data typified by the Internet-of-Things (IoT) and artificial intelligence (AI) have been increasing. Recent computer-aided manufacturing (CAM) systems have evolved so that unskilled technicians can create tool paths relatively easily with numerically controlled (NC) programs, but tool-cutting conditions used for machining cannot be automatically determined. Therefore, many unskilled technicians often set the cutting conditions based on the recommended conditions described in the tool catalog. However, given that the catalog contains large-scale data on machining technology, setting the proper conditions becomes a time-consuming and inefficient process. In this study, we aimed to construct a system to support unskilled technicians to determine the optimum machining conditions. To this end, we constructed a prediction model using a random forest machine learning method to predict the cutting conditions. It was confirmed that the prediction with the random forest method can be performed with high accuracy based on the cutting conditions recommended by the tool maker. Thus, the effectiveness of this method was verified.

Land cover change in Badung Regency 2016-2020 : An approach using machine learning method: Random Forest & Extreme Gradient Boost (XGB)

2021 ◽  
Author(s):  
Afifuddin ◽  
Siti Arfah ◽  
Dionysius Bryan Sencaki ◽  
Zilda Dona Okta Permata ◽  
Mega Novetrishka Putri ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Land Cover ◽  
Land Cover Change ◽  
Machine Learning Method ◽  
Learning Method
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