Disease Prediction System using Improved K-means Clustering Algorithm and Machine Learning

Kaur, Chandanpreet; Sharma, K.; Sohal, Asha

doi:10.26438/ijcse/v7i5.11481153

Cited by 2 publications

(1 citation statement)

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“…Deep learning classification algorithm for skin cancer also can provide a highly accurate detection rate developed by the Stanford research team (Esteva, Kuprel, Novoa & et al, 2017). Different types of diseases were investigated to apply ML algorithms for predictions (Lampropoulos, & Tsihrintzis, 2015;Verma & Ghosh, 2018;Yekkala & Dixit 2018;Papachristou & et-al, 2016;Kaur, Sharma & Sohal 2019;Harini & Natesh, 2018;Laskar & et-al, 2016). In the meantime, Jiang & et-al.…”

Section: Ai-supported Diagnosesmentioning

confidence: 99%

Learning Disease Causality Knowledge From the Web of Health Data

Reiff-Marganiec

2022

International Journal on Semantic Web and Information Systems

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Health information becomes importantly valuable for protecting public health in the current coronavirus situation. Knowledge-based information systems can play a crucial role in helping individuals to practice risk assessment and remote diagnosis. We introduce a novel approach that will develop causality-focused knowledge learning in a robust and transparent manner. Then, the machine gains the causality and probability knowledge for inference (thinking) and accurate prediction later. Besides, the hidden knowledge can be discovered beyond the existing understanding of the diseases. The whole approach is built on a Causal Probability Description Logic Framework that combines Natural Language Processing (NLP), Causality Analysis and extended Knowledge Graph (KG) technologies together. The experimental work has processed 801 diseases in total (from the UK NHS website linking with DBpedia datasets). As a result, the machine learnt comprehensive health causal knowledge and relations among the diseases, symptoms, and other facts efficiently.

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