Document clustering is related to data clustering concept which is one of data mining tasks and unsupervised classification. It is often applied to the huge data in order to make a partition based on their similarity. Initially, it used for Information Retrieval in order to improve the precision and recall from query. It is very easy to cluster with small data attributes which contains of important items. Furthermore, document clustering is very useful in retrieve information application in order to reduce the consuming time and get high precision and recall. Therefore, we propose to integrate the information retrieval method and document clustering as concept space approach. The method is known as Latent Semantic Index (LSI) approach which used Singular Vector Decomposition (SVD) or Principle Component Analysis (PCA). The aim of this method is to reduce the matrix dimension by finding the pattern in document collection with refers to concurrent of the terms. Each method is implemented to weight of term-document in vector space model (VSM) for document clustering using fuzzy c-means algorithm. Besides reduction of term-document matrix, this research also uses the cosine similarity measurement as replacement of Euclidean distance to involve in fuzzy c-means. And as a result, the performance of the proposed method is better than the existing method with f-measure around 0.91 and entropy around 0.51.

Recent healthcare research has focused a great deal of interest on using genetic data analysis to predict the risk of hypertension. This paper presents a unique method for accurately predicting the vulnerability to hypertension by utilizing single nucleotide polymorphism (SNP) data. We present a novel neural network design utilizing the adaptive moment (Adam) optimizer to describe the intricate temporal correlations in SNPs. The study used a dataset with carefully preprocessed SNP data from a broad cohort for model input. The long short-term memory (LSTM) network was methodically built and trained with hyper-parameter and fine-tuning using the Adam optimizer to converge on ideal weights. Our findings indicate encouraging predictive performance, highlighting the suggested methodology’s usefulness in determining hypertension risk factors. The result showed that the proposed method achieved stability in the performance of 89% accuracy, 96% precision, 88% recall, and 92% F1-score. Due to its higher accuracy and greater predictive power, our SNP-based LSTM methodology is superior to the conventional machine learning method. By providing a novel framework that uses genetic data to predict the risk of hypertension, this research makes substantial contribution to the field of predictive healthcare. This framework helps with early intervention and customized preventative efforts.

Background: Malaria remains a major public health concern around the world. Microscopic blood smear examination continues to be the gold standard for diagnosis; however, it requires high technical skills and expertise, limiting diagnostic accuracy in resource-poor settings. Artificial intelligence (AI) has emerged as a promising tool to support malaria detection. This systematic review provides an overview of the diagnostic performance of AI-based systems for malaria diagnosis in a clinical setting. Methods: This study followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and involved articles within the last 10 years that were collected from PubMed, ScienceDirect, Cochrane, EBSCO, and Wiley Online Library. Original articles that reported AI diagnostic accuracy with external validation were involved. The quality of each study was evaluated using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Results: Ten studies with 6754 patients were analyzed. Pooled results of sensitivity [87.7% (95% CI: 78.2–93.4)] and specificity [91.4% (95% CI: 77.3–97.1)] revealed how much the AI agrees with each method when that method is used as a gold standard. Additionally, AI achieved a sensitivity of 87.7% and a specificity of 91.4% compared to microscopy examination and a sensitivity of 90.7% and a specificity of 88.3% compared to polymerase chain reaction (PCR). Conclusions: AI-based systems improve malaria diagnosis by providing high accuracy, automation, and lower costs. Showing performance comparable to reference methods such as microscopy and PCR, AI is a promising complementary tool for malaria control.

Improving Performance for Diabetic Nephropathy Detection Using Adaptive Synthetic Sampling Data in Ensemble Method of Machine Learning Algorithms

Coal price prediction is needed as one of the supports for coal industry to make transaction. Prediction result can be used to make next budgeting for the buyer or manage the profit for the seller. We propose Support Vector Regression (SVR) method to predict coal price. Before calculating regression function there is mapping data stage, hessian matrix. Kernel for hessian matrix stage can determine accuracy of prediction. Therefore, Gaussian RBF kernel and ANOVA kernel are used and analyzed the effects. To obtain predictive results with good accuracy, testing of each parameter is performed and evaluated by mean absolute percentage error (MAPE). The averages MAPE for testing are 9,64% with Gaussian kernel and 8,38% with ANOVA kernel, which is categorized very well. The predicted results of both kernels are not too different, but the ANOVA kernel works better on this coal price data.