A Survey on Various Classification Techniques for Clinical Decision Support System

Vaghela, C.; Bhatt, Nikita; Mistry, Darshana

doi:10.5120/20498-2369

Cited by 15 publications

(8 citation statements)

References 11 publications

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“…The SVM is a supervised ML algorithm used for regression and classification tasks. It serves as a linear separator sandwiched between two data nodes to detect two different classes in the multidimensional environs [31].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Efficient Stock-Market Prediction Using Ensemble Support Vector Machine

Nti

Adekoya

Weyori

2020

Open Computer Science

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AbstractPredicting stock-price remains an important subject of discussion among financial analysts and researchers. However, the advancement in technologies such as artificial intelligence and machine learning techniques has paved the way for better and accurate prediction of stock-price in recent years. Of late, Support Vector Machines (SVM) have earned popularity among Machine Learning (ML) algorithms used for predicting stock price. However, a high percentage of studies in algorithmic investments based on SVM overlooked the overfitting nature of SVM when the input dataset is of high-noise and high-dimension. Therefore, this study proposes a novel homogeneous ensemble classifier called GASVM based on support vector machine enhanced with Genetic Algorithm (GA) for feature-selection and SVM kernel parameter optimisation for predicting the stock market. The GA was introduced in this study to achieve a simultaneous optimal of the diverse design factors of the SVM. Experiments carried out with over eleven (11) years’ stock data from the Ghana Stock Exchange (GSE) yielded compelling results. The outcome shows that the proposed model (named GASVM) outperformed other classical ML algorithms (Decision Tree (DT), Random Forest (RF) and Neural Network (NN)) in predicting a 10-day-ahead stock price movement. The proposed (GASVM) showed a better prediction accuracy of 93.7% compared with 82.3% (RF), 75.3% (DT), and 80.1% (NN). It can, therefore, be deduced from the fallouts that the proposed (GASVM) technique puts-up a practical approach feature-selection and parameter optimisation of the different design features of the SVM and thus remove the need for the labour-intensive parameter optimisation.

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Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Efficient Stock-Market Prediction Using Ensemble Support Vector Machine

Nti

Adekoya

Weyori

2020

Open Computer Science

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“…Though several machine-learning algorithms can be employed for this study, however, the Random Forest (RF) algorithm was adopted based on the assessments in the following research work. In (Luo and Zhang, 2014;Nti et al, 2020d;2019a-b;Vaghela et al, 2015), authors report that RF outperformed support vector machine SVM, AdaBoost and Artificial Neural Network (ANN). Again, the report of (Larivière and Van den Poel, 2005) shows that RF outperforms Linear Regression (LR).…”

Section: The Adopted Machine Learning Algorithmmentioning

confidence: 99%

Predicting the Remaining Lifetime of Distribution Transformers using Machine Learning

Enoch

George

Aning

2020

American Journal of Engineering and Applied Sciences

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Distribution Transformer is a crucial element in deciding the power flow in large power systems. Their better performance implies high power system efficiency and enhanced power transfer capability. However, various Distribution Transformer failures in the recent past lead to power supply disturbance and have acquired much attention from the electrical intellectuals. It is of considerable significance to accurately get the running state of distribution transformers and timely detect the existence of potential transformer faults. This project work presents a predictive model to predict the potential of a distribution transformer failing before its expected years in service. Using Random Forest machine learning techniques, we examine transformer data from August 2010 to June 2019. Our experimental results reveal that a total of 90 distribution transformers were damaged within nine years. Thus, average the company losses ten (10) transformer in a year, which amount to the US $92300-95770 per year. Also, most of the places that recorded rate of distribution transformer damage were a location that had mini and major factories around. Thus, the Sunyani Municipality recorded the highest transformer damage (12), representing 13%, followed by Mim (10). Again, lighting strike was the significant causes of transformer damage. Thus twenty-one (21) out of the ninety (90) damage transformers was caused by a lightning strike. The results further show that 33.33% of the damage transformers were with 24.75-36.75% of their life expectancy. As low as 3.33% of the damage transformers have been in service for 73% of the life expectancy. From the study results, it can be concluded that a high percentage (68.9%) of the damage transformers in the Bono, Bono East and Ahafo regions of Ghana have been in service less the half of its expected years of service. Rate-offaulty-occurrence, Type-of-faults-sustained and Tap-changer-type are the most significant factors that determine the number of years left for a distribution transformer to fail. We observed that the make of a transformer was of less importance in predicting the years left for a transformer to fail. Finally, the RMSE of 0.001639 and MAPE error of 0.001321 achieved by the proposed model shows that the proposed model fits very well to the dataset.

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“…Naïve Bayes juga digunakan untuk Clinical Decision Support System (CDSS) yang memanfaatkan clound computing technique yang berisi big medical dataset untuk menentukan diagnosis pasien (Liu, Lu, Ma, Chen, & Qin, 2016). Survey yang dilakukan menunjukkan metode Naïve Bayes termasuk salah satu metode yang sering digunakan dalam CDSS (Vaghela, Bhatt, & Mistry, 2015).…”

Section: Tinjauan Pustaka 21 Penelitian Terkaitunclassified

Penerapan Sistem Penunjang Keputusan Menggunakan Algoritma Naive Bayes Pada konsep Human Resource Information System (HRIS) (Studi kasus :Penerusan Kontrak Kerja Karyawan di PT. XYZ)

Remawati

Harsadi

Nugroho

2020

JIS

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One of the valuable assets in a company is human resources (HR). Human Resource Information System (HRIS) has emerged as one of the drivers of competitiveadvantage and strategic decision making tool. One of the HRIS task is employee recruitment. Employees become an important role. Therefore, this research is conducted forclassification of employee status determination using the Naïve Bayes methods. One of the duties of employees is to provide service to customer in the process of purchasing goods until the payment transaction process directly. Because of the large number of contract employees at the time of certain events, it requires companies to select every three months for the continuation of the work contract period for employees according to store needs so that the company's employee payroll expenses do not exceed the budget. One of the criteria is for being able to work in flexible groups between the ages of 17 and 25 for contract employees with a minimum of high school or vocational education. The purposes of this study are to design and build a Decision Support System application for the Continuation of Employee Employment Contracts Using the Naïve Bayes Method at PT. XYZ Retail. The result of the research is the application using the naïve bayes method with an accuracy rate of 90%.

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A Survey on Various Classification Techniques for Clinical Decision Support System

Cited by 15 publications

References 11 publications

Efficient Stock-Market Prediction Using Ensemble Support Vector Machine

Efficient Stock-Market Prediction Using Ensemble Support Vector Machine

Predicting the Remaining Lifetime of Distribution Transformers using Machine Learning

Penerapan Sistem Penunjang Keputusan Menggunakan Algoritma Naive Bayes Pada konsep Human Resource Information System (HRIS) (Studi kasus :Penerusan Kontrak Kerja Karyawan di PT. XYZ)

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