背景：停經症候群 (menopausal symptoms) 的健康管理為一個熱門的研究主題，其主要原因在於停經症狀對環更年期 (perimenopause) 婦女的生活品質有巨大的影響與衝擊。近年來，隨著穿戴式裝置的普及，使用者的生活歷程及部份生理資訊是可以被連續記錄下來的，而這些可貴的資料若能善加運用，例如透過長期地追蹤心跳率的變化，以追蹤健康狀況，將有助於環更年期婦女更好地管理停經症狀並減輕此族群在這個重要生命階段的身心壓力。研究目的：本研究將蒐集更年期婦女長期的心跳率資料，以機器學習的方式，建立安靜心跳率的預測模型。方法：(1)受試者：本研究將以過去所招募的1位環更年期婦女為對象，無任何與慢跑相關的慢性疾病。(2)實驗設計：本研究將使用過去受試者佩掛穿戴式裝置 (Garmin Venu SQ, Garmin, Taiwan) 期間所蒐集而得之資料，包括各式運動類型，每日行走的步數、距離、時間和各時段心跳率等，進一步使用Python進行資料整理並建立機械學習模式，建構出以前述運動行為指標、睡眠行為指標及心率指標預測隔日安靜心率之模式。(3)統計方法：以逐步迴歸進行分析和查驗安靜心跳率與其他指標之間的關聯性。結果： (1)隨機森林模型在多項性能指標上展現出較佳的表現，但整體而言，機器學習模型與統計方法之間無明顯差異。(2)特徵重要性排序顯示平均安靜心跳率為最關鍵預測因素。(3)當樣本數量達到約100天時，預測指標如R²、MAE和相關係數(r)均顯示出穩定性，進而提升模型的預測穩定性和可靠度。結論：在本研究中，機器學習模型與傳統統計方法在預測隔日安靜心率方面表現相近，數據的線性關係可能限制了機器學習複雜演算法的效益。此外，增加相關的睡眠和運動指標，對模型整體預測準確性的提升有限。

Introduction: Managing menopausal symptoms is a popular research topic due to the significant impact these symptoms have on the quality of life for women in perimenopause. With the widespread use of wearable devices, part of users’ physiological data can now be continuously recorded. This valuable information, such as long-term heart rate monitoring to track health status, can help perimenopausal women better manage their symptoms and reduce physical and mental stress during this critical life stage.
Aims: To collect heart rate data from perimenopausal women using wearable device and develop a predictive model for resting heart rate using machine learning.
Methods: The study involved perimenopausal women equipped with Garmin Venu SQ wearable devices to monitor daily steps, distance, and heart rates. Python was utilized to organize the data and develop the predictive model.
Results: The Random Forest model showed superior performance, but machine learning and statistical methods performed similarly overall. Feature importance analysis showed that the average resting heart rate is the key predictor. The model stabilized when the sample size reached about 100 days.
Conclusion: Both machine learning and traditional statistical methods have similar outcomes in predicting next-day resting heart rate. The linear data relationships may limit the effectiveness of machine learning algorithms. Adding features related to the timing and consistency of sleep and exercise into the predictive model only slightly improve accuracy.

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