Table 2.

Pathology types with used models and their strengths and weakness.

Pathology Type	Name	Models	Accuracy (%)	Strengths	Limitations	Future Developments
Liver	Hepatic fibrosis stage[16], and chronic hepatitis-B [19]	NB, RF, KNN, SVM, and NN	78.1–82.7	Liver related diseases produce large patient information, metabolomics analyses, and EHR. Deep learning algorithms help in the prediction of liver therapeutic discovery.	There is currently no complete AI system that can able to detect a couple of abnormalities overall through the human body [38].	Further studies are needed to develop an advanced deep learning algorithm to remedy greater complicated medical imaging troubles, along with ultrasound or Positron-emission tomography (PET) [18].
Pulmonary	COPD exacerbation, asthma exacerbation[25], lung cancer stages [26]	Bayesian Network, LR, SVM, NB, and PCA	62.3–76.1	Studies proposed a data-driven methodology that can help to produce COPD predictive models and asthma exacerbations. It would be useful to support both patients and physicians [39].	Even it is less cost of devices like spirometers to check lung functionality but it is not likely to replaced by quantified computed tomography.	It is highly recommended in future studies to incorporate ML models in the predictive analysis [40].
Nervous system	Dementia, Ischemic stroke lesions identification [29], late-life dementia [31], degenerative moment disorders [32]	SVM, LR, NB, RF, Hierarchical clustering analyses, and DSI	69–80	ML studies in Nervous systems can help to improve the diagnosis of Nerve system conditions	AI-based behavioral systems are still in early to understand the discrete behavior of patient chronic conditions	Future AI might be able to represent these features into one cognitive reinforcement-mastering model [41].
Diabetes	Type 2 Diabetes Mellitus [23]	LR, ANN, NB, KNN, and RF	73.2–91.6	These techniques in diabetic studies can be helpful in symptoms recognition, and disease forecasting	Technological advancements in AI need to more effective with large data sets in diabetes prediction [42]	ML applications need to produce facts on big data mining of medical data sets [42,43].
Kidney Diseases	Glomerular filtration rate estimation [24]	ANN, SVM, Regression and ensemble learning	73.1–76.0	Risk prediction can highly effective in kidney diseases	The research gap in the artificial kidney implantation needs to be addressed [44].	Many demanding situations need to be a success before it becomes a fact and a part of medical practice in nephrology.
Disease-related to muscle pains	Fibromyalgia (FM) [36]	KNN	-	In FM class division, K-means clusters can helpful for categorization of pain, clinical procedure usage, and symptom severity	KNN is a self-learner in trained data classification [45].	Future studies are needed to propose feasible algorithms to forecast FM causes.
Heart diseases	peptides for heart failure [34]	NB, and SVM	84–91	Optimized data-driven ML techniques are helped to predict heart diseases that improve total research and preventive care. Also, it will make sure that many people can happily lead a healthy lifestyle	To predict the risk quality of the heart dataset is needed in clinical practice to support high-quality datasets of heart patients.	Scientists’ are needed to propose precise models to predict the risk of heart failures [46]
Infections	Periodontitis [35]	SVM, NN	Not defined	NN and SVM algorithms are useful in the diagnosis and prediction of periodontal diseases	Lack of optimal datasets and model improvements	A computer-aided classification system can be expected to become an efficient and effective procedure for these inflectional diseases [47]