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臺北醫學大學 醫學資訊研究所碩士班 SHABBIR, SYED ABDUL所指導 卡拉姆的 以機器學習模型訓練臨床資料預測臺灣成年人肝癌與過重或肥胖之關聯性 （2021），提出MJ cable關鍵因素是什麼，來自於Liver Cancer、Overweight、Obesity、Machine Learning、Feature selection。
而第二篇論文國立臺北科技大學 管理學院管理博士班 林榮禾、吳斯偉所指導 黃玉娟的 嬰兒按摩對於嬰幼兒發展、主要照顧者及托育人員相關之研究 （2021），提出因為有 嬰兒按摩、嬰幼兒發展、親子互動關係、工作成就感的重點而找出了 MJ cable的解答。
以機器學習模型訓練臨床資料預測臺灣成年人肝癌與過重或肥胖之關聯性
為了解決MJ cable 的問題，作者卡拉姆 這樣論述：
Background: Liver cancer has become one of Taiwan’s increasingly vital public health problems due to an ageing population and environmental changes in lifestyle behaviour. The increase in the "overweight and obesity epidemic" may provide means for understanding why liver cancer is one of the few ma
lignancies with rising incidence in developed countries over the last decades. With the increased liver cancer incidence and mortality in developed countries, the prevalence of overweight and obesity has also grown markedly over the past two decades. It has been estimated that approximately 1.9 bill
ion adults are either overweight or obeseResearch Aim: The main aim of this study is to develop and validate a machine-learning algorithm (XGBoost) to establish a 1-year liver cancer risk prediction model among Taiwanese adults with overweight or obesity using clinical dataMaterials and Methods: We
conducted a retrospective study on 4,149 patients from the MJ Taipei database between 2008 and 2017. We used stepwise logistic regression with both forward selection and backward elimination to select features. Akaike Information Criterion (AIC) was used as the selection method. The performance of t
he models was assessed using accuracy, sensitivity, specificity, AUROC, and the F1 score.Results: The stepwise regression identified 13 predictors for liver cancer. There were 133 patients who developed liver cancer within one year of follow-up and 4,016 with non-liver cancer. Of those with liver ca
ncer, 80% were men. The XGBoost had an AUROC of 90%, RF of 89%, and LR of 67%. In multivariate analysis, age, gender, BMI, total bilirubin, creatinine, body fat, lactate dehydrogenase, sGOT and waist circumference acted as independent predictors of liver cancer.Conclusions: The XGBoost model had th
e optimal performance in predicting liver cancer compared to RF and LR. These findings represent an important step in the accurate prediction of liver cancer using ML techniques. If validated in prospective studies, the XGBoost model could serve as a useful approach for the prediction of liver cance
r associated with overweight or obesity and may be potentially beneficial in implementing early preventive and therapeutic measures.
嬰兒按摩對於嬰幼兒發展、主要照顧者及托育人員相關之研究
為了解決MJ cable 的問題，作者黃玉娟 這樣論述：
許多研究已證實嬰兒按摩係透過肌膚撫觸使皮膚舒緩的方式之一，不僅可滋育嬰幼兒的心靈，更能促進嬰幼兒身體健康與發展；嬰兒按摩除了直接對嬰幼兒發展有好處外，對於實施嬰兒按摩的主要照顧者和托育人員而言，也可使其情緒放鬆，增進與嬰幼兒正向的互動而產生良好的依附關係，提升主要照顧者養育的成就感與滿意度。基於上述因素，本研究的目的為探討嬰兒按摩對嬰幼兒發展、親子互動關係的影響以及主要照顧者滿意度及托育人員工作成就感之影響。其包含三個研究子題，子題一: 「嬰兒按摩對於嬰幼兒發展與親子互動關係之影響」，旨在探討嬰兒按摩對於嬰幼兒身心健康、親子關係與親職功能之影響；子題二: 「嬰兒按摩對於主要照顧者滿意度之影響
」，旨在探究嬰兒按摩是否能增進主要照顧者的滿意度；子題三: 「嬰兒按摩對於托育人員工作成就感之影響」，旨在探討嬰兒按摩透過心理所有權對托育人員工作成就感的影響。；本研究透過質性研究的深度訪談、焦點團體討論、觀察記錄、文獻分析及量化研究的問卷歸納與分析，獲得實證結果顯示：(一)嬰兒按摩有助於幼兒身心發展。(二)嬰兒按摩的課程方案有助於改善親子關係(三)嬰兒按摩有助於提升父母及家庭親職功能。(四) 實施嬰兒按摩對托育人員工作成就感有正向影響。(五) 心理所有權對實施嬰兒按摩與托育人員工作成就感之關係具有中介效果。基於上述結果，本研究得知透過嬰兒按摩可以促進嬰幼兒的發展，以及提供主要照顧者有效照顧嬰
幼兒的方法， 增進主要照顧者的信心；提高托育人員對托育機構的心理所有權，進而提升托育人員的工作成就感。以期將研究發現提出具體建議，提供幼托產業、主要照顧者、托育人員及教育單位參考。