Based on Hybrid Machine Learning
Monitoring of patient health condition is the process of regularly collecting and analyzing data on a patient's health status in order to identify any changes or trends. Hybrid Machine Learning is a type of machine learning approach that combines different types of algorithms to improve the accuracy and reliability of the predictions made.
In the context of patient health condition monitoring, hybrid machine learning can be used to analyze data from various sources such as electronic health records, sensor data, and patient-reported outcomes. This data can include information on vital signs, medications, laboratory test results, and other clinical measurements.
By combining different types of algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, hybrid machine learning can identify patterns and trends in the data that would be difficult to detect using a single type of algorithm.
Additionally, the hybrid machine learning approach can also be used to develop prediction models that can forecast the likelihood of a patient's health status, such as the risk of developing a certain condition or the probability of hospital readmission.
Monitoring of patient health condition using a hybrid machine learning approach can provide a more accurate and reliable assessment of a patient's health status by analyzing data from multiple sources and combining different types of algorithms. This can help to identify potential health issues early and improve patient outcomes.
Data mining is new powerful technology which helps in extracting hidden predictive
information from large databases and thus facilitating decision makers to make proactive,
knowledge driven decisions. The main objective of data mining process is to take out
information from a data set and transform it into an expected structure for further use . A
number of analytical tools of data mining software are used for analyzing data. This software
is used to analyze data from many different categories, dimensions or angles and to
summarize the relationships identified. A large volume of data to discover frequently occurring
patterns and correlations is one important aspect of data mining.
Intersection of artificial intelligence, machine learning, statistics, and database systems will
apply the computational process of discovering patterns in large data sets involving methods in
Data Mining refers Knowledge Discovery in Databases (KDD).Also refers to the previously
unknown and potentially useful information, nontrivial extraction of implicit from
data in databases. Therefore knowledge discovery and data mining in databases are
frequently treated as synonyms, data mining is actually part of the knowledge discovery process .
The Knowledge Discovery in Database process comprises an iterative process consisting of Data
cleaning, also known as data cleansing; it is a phase in which noise data and irrelevant data
are removed from the collection. Data integration multiple often heterogeneous, may be
combined in a common source of data.
