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LiFiBay: Noninvasive Evaluation of Liver Fibrosis by Using Bayesian Networks with Ant Colony Optimization
Ant colony optimization
Bayesian belief network
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|摘要:||使用血液生化指標(biochemical markers)來進行肝硬化程度檢測的研究已行之有年，在我們的研究中，貝氏網路(Bayesian network)被使用來進行肝硬化程度的檢測。
我們研究出來的方法不但與傳統的肝切片(Liver biopsy)檢測相比之下顯得較為簡單、經濟、方便，只需透過驗血即可得的血液生化指標便可進行檢測；且與現有同樣利用生化指標來進行肝硬化程度評估的非侵入式檢測法APRI 與 FIB-4相較之下，我們的方法有著較高的正確率。
卡方分佈(Chi-square)被用來量測兩個生化指標之間的關聯性強度，且在建立貝氏網路時可用此關聯性來建立點(node)與點之間的連結(link, arc, edge)。然而，兩指標間的相依性乃是對等的，無法明確得知各對相互關聯的指標於貝氏網路中哪些指標為親節點(parent node)或子節點，且每個親節點對子節點的最佳切分點(division point)也是未知的，為此，使用蟻群最佳化演算法在有限的時間內來尋找最佳且切分點適當的貝式網路模型以用來進行肝硬化程度的檢測。|
Noninvasive evaluation which uses biochemical data for assessing the degree of liver fibrosis has been studied for many years. In our research, Bayesian network can be used to assess the degree of liver fibrosis well. Our method, not only comparing to traditional invasive evaluation method, Liver biopsy, is simpler, more economy and convenient, but also comparing to current noninvasive method, which also uses biochemical markers-APRI and FIB-4 is more accurate. Chi-square has been used to estimate the degree of relations between each pair of biochemical markers and build links between nodes of Bayesian network according to these relations. However, the dependency examinations between markers are symmetric-which nodes are parent nodes or sub nodes of each pair of related nodes in Bayesian network can not be known with certainty, and suitable division points for parent nodes are also unknown. For the reasons mentioned above, Ant Colony Optimization can be used to search for an optimized Bayesian network model with suitable division points of each parent nodes in limited time.
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