Personalized Liver Cancer Risk Prediction Using Big Data Analytics Techniques with Image Processing Segmentation

• Published in: Computational intelligence and neuroscience Vol. 2022; no. 1; p. 8154523
• Main Authors: Jain, Anurag, Nadeem, Ahmed, Majdi Altoukhi, Huda, Jamal, Sajjad Shaukat, Atiglah, Henry kwame, Elwahsh, Haitham
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Big Data; Cancer; Cancer therapies; Cholangiocarcinoma; Cloud Computing; Computers; Data analysis; Data Science; Drug development; Drug discovery; Drugs; Electronic health records; Equipment and supplies; Feature extraction; Filaments; Gene expression; Health aspects; Humans; Image processing; Image Processing, Computer-Assisted; Image segmentation; Ligands; Liver cancer; Liver Neoplasms - diagnostic imaging; Machine learning; Massive data points; Mathematical analysis; Methods; Multiprocessing; Oncology, Experimental; Parallel processing; Performance measurement; Predictions; Risk factors; Screening; Software; Tumors; United States > US
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/8154523

A technology known as data analytics is a massively parallel processing approach that may be used to forecast a wide range of illnesses. Many scientific research methodologies have the problem of requiring a significant amount of time and processing effort, which has a negative impact on the overall performance of the system. Virtual screening (VS) is a drug discovery approach that makes use of big data techniques and is based on the concept of virtual screening. This approach is utilised for the development of novel drugs, and it is a time-consuming procedure that includes the docking of ligands in several databases in order to build the protein receptor. The proposed work is divided into two modules: image processing-based cancer segmentation and analysis using extracted features using big data analytics, and cancer segmentation and analysis using extracted features using image processing. This statistical approach is critical in the development of new drugs for the treatment of liver cancer. Machine learning methods were utilised in the prediction of liver cancer, including the MapReduce and Mahout algorithms, which were used to prefilter the set of ligand filaments before they were used in the prediction of liver cancer. This work proposes the SMRF algorithm, an improved scalable random forest algorithm built on the MapReduce foundation. Using a computer cluster or cloud computing environment, this new method categorises massive datasets. With SMRF, small amounts of data are processed and optimised over a large number of computers, allowing for the highest possible throughput. When compared to the standard random forest method, the testing findings reveal that the SMRF algorithm exhibits the same level of accuracy deterioration but exhibits superior overall performance. The accuracy range of 80 percent using the performance metrics analysis is included in the actual formulation of the medicine that is utilised for liver cancer prediction in this study.