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Unfortunately, due to insufficient resources and awareness in underdeveloped countries, proper and timely PD detection is highly challenged. Besides, all PD patients\u2019 symptoms are neither the same nor they all become pronounced at the same stage of the illness. Therefore, this work aims to combine more than one symptom (rest tremor and voice degradation) by collecting data remotely using smartphones and detect PD with the help of a cloud-based machine learning system for telemonitoring the PD patients in the developing countries.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Method<\/jats:title>\n<jats:p>This proposed system receives rest tremor and vowel phonation data acquired by smartphones with built-in accelerometer and voice recorder sensors. The data are primarily collected from diagnosed PD patients and healthy people for building and optimizing machine learning models that exhibit higher performance. After that, data from newly suspected PD patients are collected, and the trained algorithms are evaluated to detect PD. Based on the majority-vote from those algorithms, PD-detected patients are connected with a nearby neurologist for consultation. Upon receiving patients\u2019 feedback after being diagnosed by the neurologist, the system may update the model by retraining using the latest data. Also, the system requests the detected patients periodically to upload new data to track their disease progress.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Result<\/jats:title>\n<jats:p>The highest accuracy in PD detection using offline data was <jats:inline-formula><jats:alternatives><jats:tex-math>$$98.3\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n<mml:mrow>\n<mml:mn>98.3<\/mml:mn>\n<mml:mo>%<\/mml:mo>\n<\/mml:mrow>\n<\/mml:math><\/jats:alternatives><\/jats:inline-formula> from voice data and <jats:inline-formula><jats:alternatives><jats:tex-math>$$98.5\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n<mml:mrow>\n<mml:mn>98.5<\/mml:mn>\n<mml:mo>%<\/mml:mo>\n<\/mml:mrow>\n<\/mml:math><\/jats:alternatives><\/jats:inline-formula> from tremor data when used separately. In both cases, k-nearest neighbors (kNN) gave the highest accuracy over support vector machine (SVM) and naive Bayes (NB). The application of maximum relevance minimum redundancy (MRMR) feature selection method showed that by selecting different feature sets based on the patient\u2019s gender, we could improve the detection accuracy. This study\u2019s novelty is the application of ensemble averaging on the combined decisions generated from the analysis of voice and tremor data. The average accuracy of PD detection becomes <jats:inline-formula><jats:alternatives><jats:tex-math>$$99.8\\%$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n<mml:mrow>\n<mml:mn>99.8<\/mml:mn>\n<mml:mo>%<\/mml:mo>\n<\/mml:mrow>\n<\/mml:math><\/jats:alternatives><\/jats:inline-formula> when ensemble averaging was performed on majority-vote from kNN, SVM, and NB.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>The proposed system can detect PD using a cloud-based system for computation, data preserving, and regular monitoring of voice and tremor samples captured by smartphones. Thus, this system can be a solution for healthcare authorities to ensure the older population\u2019s accessibility to a better medical diagnosis system in the developing countries, especially in the pandemic situation like COVID-19, when in-person monitoring is minimal.<\/jats:p>\n<\/jats:sec>","DOI":"10.1186\/s40708-020-00113-1","type":"journal-article","created":{"date-parts":[[2020,10,22]],"date-time":"2020-10-22T13:03:03Z","timestamp":1603371783000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["Telemonitoring Parkinson\u2019s disease using machine learning by combining tremor and voice analysis"],"prefix":"10.1186","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5305-4852","authenticated-orcid":false,"given":"Md. Sakibur Rahman","family":"Sajal","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Md. 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