ML algorithms	Contributions
1. SVM	SVM classify and analyze symptoms to develop better diagnostic accuracy. The other contributions of SVM in precision medicine include identifying biomarkers of neurological and psychological diseases and analyzing SNPs to validate multiple myeloma and breast cancer. Clinical, pathological, and epidemiological data are analyzed by SVM to resist breast and cervical cancer. It analyzes clinical, molecular, and genomic data to validate oral cancer and diagnose mental disease [42–44]
2. Deep Learning	It is a commonly used algorithm in medicine. Generally, Deep Learning is utilized to analyzed images from different healthcare sectors, but it was highly employed in oncology. The algorithm was implemented to analyze lung cancer, CT scan, and MRI of the abdominal and pelvic area, colonoscopy, mammography, brain scan for brain tumors, radiation oncology, skin cancer, biopsy sample visualize, ultrasound of biopsy sample of prostate tumor, radiographs of malignant lung nodules, glioma through histopathological scanning, and biomarker data and sequencing (DNA and RNA). Moreover, it was also applied in the diagnostic process of many diseases, for instance, diabetic retinopathy, nodular BCC, histopathological anticipation in women with cytological deformations, dermal nevus and seborrheic keratosis, cardiac abnormalities, and cardiac muscle failure by analyzing MRI of ventricles of the heart [45–49]
3. Logistic Regression	This algorithm can evaluate the potential risk of several complex diseases such as breast cancer and tuberculosis. It also contributes to assessing patient survival rates and identifying cardiovascular disease. By analyzing prognostic factors, it can identify pulmonary thromboembolism (PTE) and non-lymphoma Hodgkin's diagnosis. [50–56]
4. Discriminant analysis	Application of discriminant analysis algorithm in medicine includes classification of patients for operation process, patients' symptom-relief satisfaction data, diagnosis of primary immunodeficiencies, BOLD MRI response classification to naturalistic movie stimuli, depression elements in cancer patients, and identifying protein-coding regions of cancer patients [57–63]
5. Decision Tree	This machine-learning algorithm is well applied for real-time healthcare monitoring, detecting and sensor aberrant data, data-extracting model for pollution prediction, and therapeutic decision support system. Some real-time application of decision tree algorithm includes challenges in order alternate therapies in oncology patients, identifying predictors of health outcomes, supporting clinical decisions, diagnosing hypertension through finding factors, locating genes associated with pressure ulcers (PUs) among elderly patients, therapeutic decision making in psychological patients, stratifying patient’s data in order to interpret decision making for precision medicine, finding the potential patients of telehealth services, diabetic foot amputation risk, and lastly it analyzes contents to help patients in medical decision [64–71]
6. Random Forest	This algorithm has been widely employed in several parts of the healthcare system. The reported contributions of this algorithm include prediction of metabolic pathways of individuals, predicting results of a patient’s encounter with psychiatrist, mortality prediction of ICU patients, classification and diagnosis of Alzheimer’s disease monitoring medical wireless sensors, detecting knee osteoarthritis, healthcare cost prediction, diagnosing mental illness, identifying non-medical factors related to health, predicting the risk of emergency admission, forecasting disease risks from clinical error data, finding factor accompanied with diabetic peripheral neuropathy diagnosis, identification of patients who are ready to get discharged from ICU, detecting depression Alzheimer patients, and diagnosing sleep disorders and non-assumptive diverse treatment effects [72–82]
7. Liner Regression	The reported contributions of this algorithm have been implemented in healthcare for several computational analyses and predictions, from monitoring treatment prescribing patterns, predicting hand surgery, decreasing the excess expenses of the healthcare system, analyzing imbalanced clinical cost data, detection of prognostically relevant risk factors, averaging decision making in healthcare, understanding the prevalence pattern of HIV, and ensuring its appropriateness [83–89]
8. Naïve Bayes	This algorithm is being used in distinct areas of medicine such as predicting risks by identifying Mucopolysaccharidosis type II, utilizing censored and time-to-event data, classifying EHR, shaping clinical diagnosis for decision support, extracting genome-wide data to identify Alzheimer's disease, modeling a decision related to cardiovascular disease, measuring quality healthcare services, constructing a predictive model for cancer in brain, asthma, prostate, and breast. [90–99]
9. KNN	KNN has been employed in various scientific domains, although it has just a few uses in the healthcare system. It was implemented in preserving the confidential information of clinical prediction in the e-Health cloud, pattern classification for breast cancer diagnosis, pancreatic cancer prediction using published literature, modeling diagnostic performance, detection of gastric cancer, pattern classification for health monitoring applications, medical dataset classification, and EHR data are some examples of real-time examples [100–105]
10. HMM	HMM algorithm was implemented in different areas of medicines, and its real-time contribution includes extraction of drug's side effects from online healthcare forums; decreasing the healthcare expenses; examine data on personal health check-up; observing circadian in telemetric activity data; clustering and modeling patient journey in medical; scrutinizing healthcare service utilization after injuries through transport system, analyzing infant cry signals and anticipating individuals entering countries with a large number of asynchronies [106–112]
11. Genetic Algorithm	It has vigorously contributed to the field of medicine. The reported contributions were observed in oncology, radiology, endocrinology, pediatrics, cardiology, pulmonology, surgery, infectious disease, neurology, orthopedics, gynecology, and many more