Analyzing the Performance of Machine Learning Techniques in Disease Prediction by Khongdet Phasinam, Tamal Mondal, Dony Novaliendry, Cheng-Hong Yang, Chiranjit Dutta, Mohammad Shabaz

“…There were no real-time methods for analyzing and forecasting heart disease in its early stages. For the prediction of heart disease, decision trees are used to analyze various training and evaluation datasets. …”
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Election Prediction on Twitter: A Systematic Mapping Study by Asif Khan, Huaping Zhang, Nada Boudjellal, Arshad Ahmad, Jianyun Shang, Lin Dai, Bashir Hayat

“…Social media platforms such as Facebook and Twitter carry a big load of people’s opinions about politics and leaders, which makes them a good source of information for researchers to exploit different tasks that include election predictions. Objective. Identify, categorize, and present a comprehensive overview of the approaches, techniques, and tools used in election predictions on Twitter. …”
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Connecting Patterns Inspire Link Prediction in Complex Networks by Ming-Yang Zhou, Hao Liao, Wen-Man Xiong, Xiang-Yang Wu, Zong-Wen Wei

“…Link prediction uses observed data to predict future or potential relations in complex networks. …”
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Credit Risk Prediction Using Fuzzy Immune Learning by Ehsan Kamalloo, Mohammad Saniee Abadeh

“…Therefore, every bank needs to predict the credit risks of its customers. Credit risk prediction has been widely studied in the field of data mining as a classification problem. …”
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Theoretical Prediction of Ultrasonic Velocity in Organic Liquid Mixtures by R. Uvarani, S. Punitha

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Tweetluenza: Predicting Flu Trends from Twitter Data by Balsam Alkouz, Zaher Al Aghbari, Jemal Hussien Abawajy

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Prediction of fertilizer in horticulture through IoT enabled technology by Agyan Kumar, Antonio Clecio Fontelles Thomaz

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Polygenic prediction of human longevity on the supposition of pervasive pleiotropy by M. Reza Jabalameli, Jhih-Rong Lin, Quanwei Zhang, Zhen Wang, Joydeep Mitra, Nha Nguyen, Tina Gao, Mark Khusidman, Sanish Sathyan, Gil Atzmon, Sofiya Milman, Jan Vijg, Nir Barzilai, Zhengdong D. Zhang

“…We showed that coefficients derived from our ensemble model could potentially reveal an interesting pattern of genomic pleiotropy specific to lifespan. We assessed the predictive performance of our model for distinguishing the enrichment of exceptional longevity among long-lived individuals in two replication cohorts (the Scripps Wellderly cohort and the Medical Genome Reference Bank (MRGB)) and showed that the median lifespan in the highest decile of our composite prognostic index is up to 4.8 years longer. …”
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Comparisons of Machine Learning Models for Prediction of Susceptibility to Diabetes by Jiao Yutian

“…While SVM has the highest precision, it performs poorly on recall, limiting its overall performance on diabetes prediction. On the contrary, LR and RF achieved good results in the F-score, making them outperform the other models in terms of overall performance score in predicting diabetes.…”
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On the Prediction of the Sideslip Angle Using Dynamic Neural Networks by Raffaele Marotta, Salvatore Strano, Mario Terzo, Ciro Tordela

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Predicting the current and future distributions of Australian Loranthaceae mistletoes by Carol A. Wilson, Kenneth Trang

“…Although Loranthaceae mistletoes are distributed throughout Australia, our results show distributional differences among haustorial types across biomes and identify their actual and predicted ecological niches based on temperature and precipitation. …”
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Evaluation of genomic and phenomic prediction for application in apple breeding by Michaela Jung, Marius Hodel, Andrea Knauf, Daniela Kupper, Markus Neuditschko, Simone Bühlmann-Schütz, Bruno Studer, Andrea Patocchi, Giovanni AL Broggini

“…Abstract Background Apple breeding schemes can be improved by using genomic prediction models to forecast the performance of breeding material. …”
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Extracting Physicochemical Features to Predict Protein Secondary Structure by Yin-Fu Huang, Shu-Ying Chen

“…We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. …”
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Generalized Linear Spatial Models to Predict Slate Exploitability by Angeles Saavedra, Javier Taboada, María Araújo, Eduardo Giráldez

“…Studying the residuals and comparing the prediction capacities of the two models lead us to conclude that the GLSM is more appropriate when the response variable presents spatial distribution.…”
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The Multivariate Fusion Distribution Characteristics in Physician Demand Prediction by Jiazhen Zhang, Wei Chen, Xiulai Wang

“…Aiming at the optimization of the big data infrastructure in China’s healthcare system, this study proposes a lightweight time series physician demand prediction model, which is especially suitable for the field of telemedicine. …”
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Predicting survival in malignant glioma using artificial intelligence by Wireko Andrew Awuah, Adam Ben-Jaafar, Subham Roy, Princess Afia Nkrumah-Boateng, Joecelyn Kirani Tan, Toufik Abdul-Rahman, Oday Atallah

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Prediction of C Regulatory Elements Using Statistical Analysis by Ping Qiu, Wei Ding, Ying Jiang, Jonathan Greene, Luquan Wang

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Food Sales Prediction Using MLP, RANSAC, and Bagging by Hussam Mezher Merdas, Ayad Hameed Mousa

“…Finally, identified the best dataset that gives excellent prediction results with these algorithms. The results showed that the first dataset gave ideal accuracy by using the Bagging regression algorithm, unlike the second dataset with medium correlation and the third dataset with weak correlation. …”
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Investigation of the Predictive Factors of Recurrent Ovarian Mucinous Cystadenoma by Aykut Ozcan, Meric Balikoglu, Kaan Okan Alkan, Varol Gulseren

“… OBJECTIVE: Rapid growth patterns and recurrence with variable rates have been reported in the literature, making mucinous cystadenomas different from other ovarian benign neoplasms. The study aimed to predict and prevent recurrence based on the obtained results. …”
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Anatomy-Informed Multimodal Learning for Myocardial Infarction Prediction by Ivan-Daniel Sievering, Ortal Senouf, Thabo Mahendiran, David Nanchen, Stephane Fournier, Olivier Muller, Pascal Frossard, Emmanuel Abbe, Dorina Thanou

“…<italic>Goal:</italic> In patients with coronary artery disease, the prediction of future cardiac events such as myocardial infarction (MI) remains a major challenge. …”
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